mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-28 19:00:59 +03:00
Add feature to be able to chain prompt values
This commit is contained in:
Henry
@@ -3,7 +3,7 @@
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"nodes": [
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{
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"width": 300,
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"height": 360,
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"height": 366,
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"id": "promptTemplate_0",
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"position": {
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"x": 294.38456937448433,
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@@ -50,7 +50,7 @@
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},
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{
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"width": 300,
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"height": 886,
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"height": 905,
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"id": "fewShotPromptTemplate_0",
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"position": {
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"x": 719.2200337843097,
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@@ -223,11 +223,11 @@
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},
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{
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"width": 300,
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"height": 461,
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"height": 592,
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"id": "llmChain_0",
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||||
"position": {
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"x": 1499.2654451385026,
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"y": 356.3275374721362
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"x": 1489.0277667172852,
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"y": 357.461975349771
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},
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"type": "customNode",
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"data": {
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@@ -239,13 +239,24 @@
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"category": "Chains",
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"description": "Chain to run queries against LLMs",
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"inputParams": [
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{
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"label": "Chain Name",
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"name": "chainName",
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"type": "string",
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"placeholder": "Task Creation Chain",
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"optional": true,
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"id": "llmChain_0-input-chainName-string"
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},
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{
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"label": "Format Prompt Values",
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"name": "promptValues",
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"type": "string",
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"rows": 5,
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"placeholder": "{\n \"input_language\": \"English\",\n \"output_language\": \"French\"\n}",
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"optional": true
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"optional": true,
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"acceptVariable": true,
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"list": true,
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"id": "llmChain_0-input-promptValues-string"
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}
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],
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"inputAnchors": [
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@@ -265,22 +276,40 @@
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"inputs": {
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"model": "{{openAI_0.data.instance}}",
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"prompt": "{{fewShotPromptTemplate_0.data.instance}}",
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"chainName": "",
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"promptValues": ""
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},
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"outputAnchors": [
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{
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"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
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"name": "llmChain",
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"label": "LLMChain",
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"type": "LLMChain | BaseChain"
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"name": "output",
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"label": "Output",
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"type": "options",
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"options": [
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{
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"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
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"name": "llmChain",
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"label": "LLM Chain",
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"type": "LLMChain | BaseChain"
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},
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{
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"id": "llmChain_0-output-outputPrediction-string",
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"name": "outputPrediction",
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"label": "Output Prediction",
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"type": "string"
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}
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],
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"default": "llmChain"
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}
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],
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"outputs": {
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"output": "llmChain"
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},
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"selected": false
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},
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"selected": false,
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"positionAbsolute": {
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"x": 1499.2654451385026,
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"y": 356.3275374721362
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||||
"x": 1489.0277667172852,
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"y": 357.461975349771
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},
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"dragging": false
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}
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@@ -0,0 +1,508 @@
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{
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"description": "Use output from a chain as prompt for another chain",
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"nodes": [
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{
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"width": 300,
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"height": 592,
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"id": "llmChain_0",
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"position": {
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"x": 586.058087758348,
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"y": 109.99914917840562
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},
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"type": "customNode",
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"data": {
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"id": "llmChain_0",
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"label": "LLM Chain",
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"name": "llmChain",
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"type": "LLMChain",
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"baseClasses": ["LLMChain", "BaseChain"],
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"category": "Chains",
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"description": "Chain to run queries against LLMs",
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"inputParams": [
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{
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"label": "Chain Name",
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"name": "chainName",
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"type": "string",
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"placeholder": "Task Creation Chain",
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"optional": true,
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"id": "llmChain_0-input-chainName-string"
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},
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{
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"label": "Format Prompt Values",
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"name": "promptValues",
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"type": "string",
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"rows": 5,
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"placeholder": "{\n \"input_language\": \"English\",\n \"output_language\": \"French\"\n}",
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"optional": true,
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"acceptVariable": true,
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"list": true,
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"id": "llmChain_0-input-promptValues-string"
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}
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],
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"inputAnchors": [
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{
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"label": "Language Model",
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"name": "model",
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"type": "BaseLanguageModel",
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"id": "llmChain_0-input-model-BaseLanguageModel"
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},
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{
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"label": "Prompt",
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"name": "prompt",
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"type": "BasePromptTemplate",
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"id": "llmChain_0-input-prompt-BasePromptTemplate"
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}
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],
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"inputs": {
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"model": "{{openAI_0.data.instance}}",
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"prompt": "{{promptTemplate_0.data.instance}}",
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"chainName": "FirstChain",
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"promptValues": "{\n \"objective\": \"{{question}}\"\n}"
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},
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"outputAnchors": [
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{
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"name": "output",
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"label": "Output",
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"type": "options",
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"options": [
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{
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"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
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"name": "llmChain",
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"label": "LLM Chain",
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"type": "LLMChain | BaseChain"
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},
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{
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"id": "llmChain_0-output-outputPrediction-string",
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"name": "outputPrediction",
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"label": "Output Prediction",
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"type": "string"
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}
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],
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"default": "llmChain"
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}
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],
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"outputs": {
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"output": "outputPrediction"
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},
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"selected": false
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},
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"selected": false,
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"positionAbsolute": {
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"x": 586.058087758348,
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"y": 109.99914917840562
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||||
},
|
||||
"dragging": false
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||||
},
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||||
{
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||||
"width": 300,
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||||
"height": 366,
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"id": "promptTemplate_0",
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||||
"position": {
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||||
"x": 231.20329590069747,
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"y": 313.54994365714185
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},
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"type": "customNode",
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"data": {
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"id": "promptTemplate_0",
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"label": "Prompt Template",
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"name": "promptTemplate",
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"type": "PromptTemplate",
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"baseClasses": ["PromptTemplate", "BaseStringPromptTemplate", "BasePromptTemplate"],
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"category": "Prompts",
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"description": "Schema to represent a basic prompt for an LLM",
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"inputParams": [
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{
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"label": "Template",
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"name": "template",
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"type": "string",
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"rows": 5,
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"placeholder": "What is a good name for a company that makes {product}?",
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"id": "promptTemplate_0-input-template-string"
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}
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],
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"inputAnchors": [],
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"inputs": {
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"template": "You are an AI who performs one task based on the following objective: {objective}.\nRespond with how you would complete this task:"
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},
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"outputAnchors": [
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{
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"id": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
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"name": "promptTemplate",
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"label": "PromptTemplate",
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"type": "PromptTemplate | BaseStringPromptTemplate | BasePromptTemplate"
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}
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||||
],
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"outputs": {},
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||||
"selected": false
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||||
},
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||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 231.20329590069747,
|
||||
"y": 313.54994365714185
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
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||||
"height": 592,
|
||||
"id": "llmChain_1",
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||||
"position": {
|
||||
"x": 1637.4327907249694,
|
||||
"y": 127.71255193457947
|
||||
},
|
||||
"type": "customNode",
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||||
"data": {
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||||
"id": "llmChain_1",
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||||
"label": "LLM Chain",
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||||
"name": "llmChain",
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||||
"type": "LLMChain",
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||||
"baseClasses": ["LLMChain", "BaseChain"],
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||||
"category": "Chains",
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||||
"description": "Chain to run queries against LLMs",
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"inputParams": [
|
||||
{
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||||
"label": "Chain Name",
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"name": "chainName",
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"type": "string",
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"placeholder": "Task Creation Chain",
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"optional": true,
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"id": "llmChain_1-input-chainName-string"
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},
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{
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"label": "Format Prompt Values",
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"name": "promptValues",
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"type": "string",
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"rows": 5,
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"placeholder": "{\n \"input_language\": \"English\",\n \"output_language\": \"French\"\n}",
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"optional": true,
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||||
"acceptVariable": true,
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||||
"list": true,
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||||
"id": "llmChain_1-input-promptValues-string"
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||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Language Model",
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||||
"name": "model",
|
||||
"type": "BaseLanguageModel",
|
||||
"id": "llmChain_1-input-model-BaseLanguageModel"
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||||
},
|
||||
{
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||||
"label": "Prompt",
|
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"name": "prompt",
|
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"type": "BasePromptTemplate",
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"id": "llmChain_1-input-prompt-BasePromptTemplate"
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||||
}
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],
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"inputs": {
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"model": "{{openAI_0.data.instance}}",
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"prompt": "{{promptTemplate_1.data.instance}}",
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"chainName": "FinalChain",
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"promptValues": "{\n \"objective\": \"{{question}}\",\n \"result\": \"{{llmChain_0.data.instance}}\"\n}"
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},
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"outputAnchors": [
|
||||
{
|
||||
"name": "output",
|
||||
"label": "Output",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"id": "llmChain_1-output-llmChain-LLMChain|BaseChain",
|
||||
"name": "llmChain",
|
||||
"label": "LLM Chain",
|
||||
"type": "LLMChain | BaseChain"
|
||||
},
|
||||
{
|
||||
"id": "llmChain_1-output-outputPrediction-string",
|
||||
"name": "outputPrediction",
|
||||
"label": "Output Prediction",
|
||||
"type": "string"
|
||||
}
|
||||
],
|
||||
"default": "llmChain"
|
||||
}
|
||||
],
|
||||
"outputs": {
|
||||
"output": "llmChain"
|
||||
},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 1637.4327907249694,
|
||||
"y": 127.71255193457947
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 366,
|
||||
"id": "promptTemplate_1",
|
||||
"position": {
|
||||
"x": 950.292796637893,
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"y": 62.31864791878181
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},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "promptTemplate_1",
|
||||
"label": "Prompt Template",
|
||||
"name": "promptTemplate",
|
||||
"type": "PromptTemplate",
|
||||
"baseClasses": ["PromptTemplate", "BaseStringPromptTemplate", "BasePromptTemplate"],
|
||||
"category": "Prompts",
|
||||
"description": "Schema to represent a basic prompt for an LLM",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Template",
|
||||
"name": "template",
|
||||
"type": "string",
|
||||
"rows": 5,
|
||||
"placeholder": "What is a good name for a company that makes {product}?",
|
||||
"id": "promptTemplate_1-input-template-string"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [],
|
||||
"inputs": {
|
||||
"template": "You are a task creation AI that uses the result of an execution agent to create new tasks with the following objective: {objective}.\nThe last completed task has the result: {result}.\nBased on the result, create new tasks to be completed by the AI system that do not overlap with result.\nReturn the tasks as an array."
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "promptTemplate_1-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
|
||||
"name": "promptTemplate",
|
||||
"label": "PromptTemplate",
|
||||
"type": "PromptTemplate | BaseStringPromptTemplate | BasePromptTemplate"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 950.292796637893,
|
||||
"y": 62.31864791878181
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 472,
|
||||
"id": "openAI_0",
|
||||
"position": {
|
||||
"x": 225.7603660247592,
|
||||
"y": -193.45016241085625
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "openAI_0",
|
||||
"label": "OpenAI",
|
||||
"name": "openAI",
|
||||
"type": "OpenAI",
|
||||
"baseClasses": ["OpenAI", "BaseLLM", "BaseLanguageModel"],
|
||||
"category": "LLMs",
|
||||
"description": "Wrapper around OpenAI large language models",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "OpenAI Api Key",
|
||||
"name": "openAIApiKey",
|
||||
"type": "password",
|
||||
"id": "openAI_0-input-openAIApiKey-password"
|
||||
},
|
||||
{
|
||||
"label": "Model Name",
|
||||
"name": "modelName",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"label": "text-davinci-003",
|
||||
"name": "text-davinci-003"
|
||||
},
|
||||
{
|
||||
"label": "text-davinci-002",
|
||||
"name": "text-davinci-002"
|
||||
},
|
||||
{
|
||||
"label": "text-curie-001",
|
||||
"name": "text-curie-001"
|
||||
},
|
||||
{
|
||||
"label": "text-babbage-001",
|
||||
"name": "text-babbage-001"
|
||||
}
|
||||
],
|
||||
"default": "text-davinci-003",
|
||||
"optional": true,
|
||||
"id": "openAI_0-input-modelName-options"
|
||||
},
|
||||
{
|
||||
"label": "Temperature",
|
||||
"name": "temperature",
|
||||
"type": "number",
|
||||
"default": 0.7,
|
||||
"optional": true,
|
||||
"id": "openAI_0-input-temperature-number"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [],
|
||||
"inputs": {
|
||||
"modelName": "text-davinci-003",
|
||||
"temperature": "0"
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel",
|
||||
"name": "openAI",
|
||||
"label": "OpenAI",
|
||||
"type": "OpenAI | BaseLLM | BaseLanguageModel"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"dragging": false,
|
||||
"positionAbsolute": {
|
||||
"x": 225.7603660247592,
|
||||
"y": -193.45016241085625
|
||||
}
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 472,
|
||||
"id": "openAI_1",
|
||||
"position": {
|
||||
"x": 1275.7643968219816,
|
||||
"y": -197.07668364123862
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "openAI_1",
|
||||
"label": "OpenAI",
|
||||
"name": "openAI",
|
||||
"type": "OpenAI",
|
||||
"baseClasses": ["OpenAI", "BaseLLM", "BaseLanguageModel"],
|
||||
"category": "LLMs",
|
||||
"description": "Wrapper around OpenAI large language models",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "OpenAI Api Key",
|
||||
"name": "openAIApiKey",
|
||||
"type": "password",
|
||||
"id": "openAI_0-input-openAIApiKey-password"
|
||||
},
|
||||
{
|
||||
"label": "Model Name",
|
||||
"name": "modelName",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"label": "text-davinci-003",
|
||||
"name": "text-davinci-003"
|
||||
},
|
||||
{
|
||||
"label": "text-davinci-002",
|
||||
"name": "text-davinci-002"
|
||||
},
|
||||
{
|
||||
"label": "text-curie-001",
|
||||
"name": "text-curie-001"
|
||||
},
|
||||
{
|
||||
"label": "text-babbage-001",
|
||||
"name": "text-babbage-001"
|
||||
}
|
||||
],
|
||||
"default": "text-davinci-003",
|
||||
"optional": true,
|
||||
"id": "openAI_0-input-modelName-options"
|
||||
},
|
||||
{
|
||||
"label": "Temperature",
|
||||
"name": "temperature",
|
||||
"type": "number",
|
||||
"default": 0.7,
|
||||
"optional": true,
|
||||
"id": "openAI_0-input-temperature-number"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [],
|
||||
"inputs": {
|
||||
"modelName": "text-davinci-003",
|
||||
"temperature": "0"
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel",
|
||||
"name": "openAI",
|
||||
"label": "OpenAI",
|
||||
"type": "OpenAI | BaseLLM | BaseLanguageModel"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"dragging": false,
|
||||
"positionAbsolute": {
|
||||
"x": 1275.7643968219816,
|
||||
"y": -197.07668364123862
|
||||
}
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": "promptTemplate_0",
|
||||
"sourceHandle": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
|
||||
"target": "llmChain_0",
|
||||
"targetHandle": "llmChain_0-input-prompt-BasePromptTemplate",
|
||||
"type": "buttonedge",
|
||||
"id": "promptTemplate_0-promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate-llmChain_0-llmChain_0-input-prompt-BasePromptTemplate",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "openAI_0",
|
||||
"sourceHandle": "openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel",
|
||||
"target": "llmChain_0",
|
||||
"targetHandle": "llmChain_0-input-model-BaseLanguageModel",
|
||||
"type": "buttonedge",
|
||||
"id": "openAI_0-openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel-llmChain_0-llmChain_0-input-model-BaseLanguageModel",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "promptTemplate_1",
|
||||
"sourceHandle": "promptTemplate_1-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
|
||||
"target": "llmChain_1",
|
||||
"targetHandle": "llmChain_1-input-prompt-BasePromptTemplate",
|
||||
"type": "buttonedge",
|
||||
"id": "promptTemplate_1-promptTemplate_1-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate-llmChain_1-llmChain_1-input-prompt-BasePromptTemplate",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "llmChain_0",
|
||||
"sourceHandle": "llmChain_0-output-outputPrediction-string",
|
||||
"target": "llmChain_1",
|
||||
"targetHandle": "llmChain_1-input-promptValues-string",
|
||||
"type": "buttonedge",
|
||||
"id": "llmChain_0-llmChain_0-output-outputPrediction-string-llmChain_1-llmChain_1-input-promptValues-string",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "openAI_1",
|
||||
"sourceHandle": "openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel",
|
||||
"target": "llmChain_1",
|
||||
"targetHandle": "llmChain_1-input-model-BaseLanguageModel",
|
||||
"type": "buttonedge",
|
||||
"id": "openAI_1-openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel-llmChain_1-llmChain_1-input-model-BaseLanguageModel",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -81,7 +81,7 @@
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 360,
|
||||
"height": 366,
|
||||
"id": "promptTemplate_0",
|
||||
"position": {
|
||||
"x": 970.576876549135,
|
||||
@@ -128,11 +128,11 @@
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 461,
|
||||
"height": 592,
|
||||
"id": "llmChain_0",
|
||||
"position": {
|
||||
"x": 1414.1175742139496,
|
||||
"y": 340.4040954840462
|
||||
"x": 1386.5063477084716,
|
||||
"y": 211.47670100294192
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
@@ -144,13 +144,24 @@
|
||||
"category": "Chains",
|
||||
"description": "Chain to run queries against LLMs",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Chain Name",
|
||||
"name": "chainName",
|
||||
"type": "string",
|
||||
"placeholder": "Task Creation Chain",
|
||||
"optional": true,
|
||||
"id": "llmChain_0-input-chainName-string"
|
||||
},
|
||||
{
|
||||
"label": "Format Prompt Values",
|
||||
"name": "promptValues",
|
||||
"type": "string",
|
||||
"rows": 5,
|
||||
"placeholder": "{\n \"input_language\": \"English\",\n \"output_language\": \"French\"\n}",
|
||||
"optional": true
|
||||
"optional": true,
|
||||
"acceptVariable": true,
|
||||
"list": true,
|
||||
"id": "llmChain_0-input-promptValues-string"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
@@ -170,38 +181,45 @@
|
||||
"inputs": {
|
||||
"model": "{{openAI_0.data.instance}}",
|
||||
"prompt": "{{promptTemplate_0.data.instance}}",
|
||||
"chainName": "CompanyName Chain",
|
||||
"promptValues": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
|
||||
"name": "llmChain",
|
||||
"label": "LLMChain",
|
||||
"type": "LLMChain | BaseChain"
|
||||
"name": "output",
|
||||
"label": "Output",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
|
||||
"name": "llmChain",
|
||||
"label": "LLM Chain",
|
||||
"type": "LLMChain | BaseChain"
|
||||
},
|
||||
{
|
||||
"id": "llmChain_0-output-outputPrediction-string",
|
||||
"name": "outputPrediction",
|
||||
"label": "Output Prediction",
|
||||
"type": "string"
|
||||
}
|
||||
],
|
||||
"default": "llmChain"
|
||||
}
|
||||
],
|
||||
"outputs": {
|
||||
"output": "llmChain"
|
||||
},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 1414.1175742139496,
|
||||
"y": 340.4040954840462
|
||||
"x": 1386.5063477084716,
|
||||
"y": 211.47670100294192
|
||||
},
|
||||
"dragging": false
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": "promptTemplate_0",
|
||||
"sourceHandle": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
|
||||
"target": "llmChain_0",
|
||||
"targetHandle": "llmChain_0-input-prompt-BasePromptTemplate",
|
||||
"type": "buttonedge",
|
||||
"id": "promptTemplate_0-promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate-llmChain_0-llmChain_0-input-prompt-BasePromptTemplate",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "openAI_0",
|
||||
"sourceHandle": "openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel",
|
||||
@@ -212,6 +230,17 @@
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "promptTemplate_0",
|
||||
"sourceHandle": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate",
|
||||
"target": "llmChain_0",
|
||||
"targetHandle": "llmChain_0-input-prompt-BasePromptTemplate",
|
||||
"type": "buttonedge",
|
||||
"id": "promptTemplate_0-promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate-llmChain_0-llmChain_0-input-prompt-BasePromptTemplate",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
@@ -1,13 +1,14 @@
|
||||
{
|
||||
"description": "Language translation using LLM Chain with a Chat Prompt Template and Chat Model",
|
||||
|
||||
"nodes": [
|
||||
{
|
||||
"width": 300,
|
||||
"height": 460,
|
||||
"height": 473,
|
||||
"id": "chatPromptTemplate_0",
|
||||
"position": {
|
||||
"x": 524,
|
||||
"y": 237
|
||||
"x": 906.3845860429262,
|
||||
"y": 522.7223115041937
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
@@ -52,8 +53,8 @@
|
||||
"selected": false,
|
||||
"dragging": false,
|
||||
"positionAbsolute": {
|
||||
"x": 524,
|
||||
"y": 237
|
||||
"x": 906.3845860429262,
|
||||
"y": 522.7223115041937
|
||||
}
|
||||
},
|
||||
{
|
||||
@@ -61,8 +62,8 @@
|
||||
"height": 472,
|
||||
"id": "chatOpenAI_0",
|
||||
"position": {
|
||||
"x": 855.1997276913991,
|
||||
"y": 24.090553068402556
|
||||
"x": 909.2168811101023,
|
||||
"y": 10.159813502526418
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
@@ -133,18 +134,18 @@
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 855.1997276913991,
|
||||
"y": 24.090553068402556
|
||||
"x": 909.2168811101023,
|
||||
"y": 10.159813502526418
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 461,
|
||||
"height": 592,
|
||||
"id": "llmChain_0",
|
||||
"position": {
|
||||
"x": 1192.2235692202612,
|
||||
"y": 361.71736677076257
|
||||
"x": 1318.8661313433918,
|
||||
"y": 323.51085023894643
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
@@ -156,13 +157,24 @@
|
||||
"category": "Chains",
|
||||
"description": "Chain to run queries against LLMs",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Chain Name",
|
||||
"name": "chainName",
|
||||
"type": "string",
|
||||
"placeholder": "Task Creation Chain",
|
||||
"optional": true,
|
||||
"id": "llmChain_0-input-chainName-string"
|
||||
},
|
||||
{
|
||||
"label": "Format Prompt Values",
|
||||
"name": "promptValues",
|
||||
"type": "string",
|
||||
"rows": 5,
|
||||
"placeholder": "{\n \"input_language\": \"English\",\n \"output_language\": \"French\"\n}",
|
||||
"optional": true
|
||||
"optional": true,
|
||||
"acceptVariable": true,
|
||||
"list": true,
|
||||
"id": "llmChain_0-input-promptValues-string"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
@@ -182,22 +194,40 @@
|
||||
"inputs": {
|
||||
"model": "{{chatOpenAI_0.data.instance}}",
|
||||
"prompt": "{{chatPromptTemplate_0.data.instance}}",
|
||||
"chainName": "",
|
||||
"promptValues": "{\n \"input_language\": \"English\",\n \"output_language\": \"French\"\n}"
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
|
||||
"name": "llmChain",
|
||||
"label": "LLMChain",
|
||||
"type": "LLMChain | BaseChain"
|
||||
"name": "output",
|
||||
"label": "Output",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"id": "llmChain_0-output-llmChain-LLMChain|BaseChain",
|
||||
"name": "llmChain",
|
||||
"label": "LLM Chain",
|
||||
"type": "LLMChain | BaseChain"
|
||||
},
|
||||
{
|
||||
"id": "llmChain_0-output-outputPrediction-string",
|
||||
"name": "outputPrediction",
|
||||
"label": "Output Prediction",
|
||||
"type": "string"
|
||||
}
|
||||
],
|
||||
"default": "llmChain"
|
||||
}
|
||||
],
|
||||
"outputs": {
|
||||
"output": "llmChain"
|
||||
},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 1192.2235692202612,
|
||||
"y": 361.71736677076257
|
||||
"x": 1318.8661313433918,
|
||||
"y": 323.51085023894643
|
||||
},
|
||||
"dragging": false
|
||||
}
|
||||
|
||||
@@ -1,9 +1,8 @@
|
||||
import { INodeData } from 'flowise-components'
|
||||
import { IActiveChatflows } from './Interface'
|
||||
import { IActiveChatflows, INodeData } from './Interface'
|
||||
|
||||
/**
|
||||
* This pool is to keep track of active test triggers (event listeners),
|
||||
* so we can clear the event listeners whenever user refresh or exit page
|
||||
* This pool is to keep track of active chatflow pools
|
||||
* so we can prevent building langchain flow all over again
|
||||
*/
|
||||
export class ChatflowPool {
|
||||
activeChatflows: IActiveChatflows = {}
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
import { INode, INodeData } from 'flowise-components'
|
||||
import { INode, INodeData as INodeDataFromComponent, INodeParams } from 'flowise-components'
|
||||
|
||||
export type MessageType = 'apiMessage' | 'userMessage'
|
||||
|
||||
@@ -38,6 +38,12 @@ export interface INodeDirectedGraph {
|
||||
[key: string]: string[]
|
||||
}
|
||||
|
||||
export interface INodeData extends INodeDataFromComponent {
|
||||
inputAnchors: INodeParams[]
|
||||
inputParams: INodeParams[]
|
||||
outputAnchors: INodeParams[]
|
||||
}
|
||||
|
||||
export interface IReactFlowNode {
|
||||
id: string
|
||||
position: {
|
||||
|
||||
@@ -4,15 +4,22 @@ import cors from 'cors'
|
||||
import http from 'http'
|
||||
import * as fs from 'fs'
|
||||
|
||||
import { IChatFlow, IncomingInput, IReactFlowNode, IReactFlowObject } from './Interface'
|
||||
import { getNodeModulesPackagePath, getStartingNodes, buildLangchain, getEndingNode, constructGraphs } from './utils'
|
||||
import { IChatFlow, IncomingInput, IReactFlowNode, IReactFlowObject, INodeData } from './Interface'
|
||||
import {
|
||||
getNodeModulesPackagePath,
|
||||
getStartingNodes,
|
||||
buildLangchain,
|
||||
getEndingNode,
|
||||
constructGraphs,
|
||||
resolveVariables,
|
||||
checkIfFlowNeedToRebuild
|
||||
} from './utils'
|
||||
import { cloneDeep } from 'lodash'
|
||||
import { getDataSource } from './DataSource'
|
||||
import { NodesPool } from './NodesPool'
|
||||
import { ChatFlow } from './entity/ChatFlow'
|
||||
import { ChatMessage } from './entity/ChatMessage'
|
||||
import { ChatflowPool } from './ChatflowPool'
|
||||
import { INodeData } from 'flowise-components'
|
||||
|
||||
export class App {
|
||||
app: express.Application
|
||||
@@ -196,44 +203,61 @@ export class App {
|
||||
|
||||
let nodeToExecuteData: INodeData
|
||||
|
||||
const chatflow = await this.AppDataSource.getRepository(ChatFlow).findOneBy({
|
||||
id: chatflowid
|
||||
})
|
||||
if (!chatflow) return res.status(404).send(`Chatflow ${chatflowid} not found`)
|
||||
|
||||
const flowData = chatflow.flowData
|
||||
const parsedFlowData: IReactFlowObject = JSON.parse(flowData)
|
||||
const nodes = parsedFlowData.nodes
|
||||
const edges = parsedFlowData.edges
|
||||
|
||||
// Check if node data exists in pool && not out of sync, prevent building whole flow again
|
||||
if (
|
||||
Object.prototype.hasOwnProperty.call(this.chatflowPool.activeChatflows, chatflowid) &&
|
||||
this.chatflowPool.activeChatflows[chatflowid].inSync
|
||||
this.chatflowPool.activeChatflows[chatflowid].inSync &&
|
||||
!checkIfFlowNeedToRebuild(nodes, this.chatflowPool.activeChatflows[chatflowid].endingNodeData)
|
||||
) {
|
||||
nodeToExecuteData = this.chatflowPool.activeChatflows[chatflowid].endingNodeData
|
||||
} else {
|
||||
const chatflow = await this.AppDataSource.getRepository(ChatFlow).findOneBy({
|
||||
id: chatflowid
|
||||
})
|
||||
if (!chatflow) return res.status(404).send(`Chatflow ${chatflowid} not found`)
|
||||
|
||||
const flowData = chatflow.flowData
|
||||
const parsedFlowData: IReactFlowObject = JSON.parse(flowData)
|
||||
|
||||
/*** Get Ending Node with Directed Graph ***/
|
||||
const { graph, nodeDependencies } = constructGraphs(parsedFlowData.nodes, parsedFlowData.edges)
|
||||
const { graph, nodeDependencies } = constructGraphs(nodes, edges)
|
||||
const directedGraph = graph
|
||||
const endingNodeId = getEndingNode(nodeDependencies, directedGraph)
|
||||
if (!endingNodeId) return res.status(500).send(`Ending node must be either a Chain or Agent`)
|
||||
|
||||
const endingNodeData = nodes.find((nd) => nd.id === endingNodeId)?.data
|
||||
if (!endingNodeData) return res.status(500).send(`Ending node must be either a Chain or Agent`)
|
||||
|
||||
if (!Object.values(endingNodeData.outputs ?? {}).includes(endingNodeData.name)) {
|
||||
return res
|
||||
.status(500)
|
||||
.send(
|
||||
`Output of ${endingNodeData.label} (${endingNodeData.id}) must be ${endingNodeData.label}, can't be an Output Prediction`
|
||||
)
|
||||
}
|
||||
|
||||
/*** Get Starting Nodes with Non-Directed Graph ***/
|
||||
const constructedObj = constructGraphs(parsedFlowData.nodes, parsedFlowData.edges, true)
|
||||
const constructedObj = constructGraphs(nodes, edges, true)
|
||||
const nonDirectedGraph = constructedObj.graph
|
||||
const { startingNodeIds, depthQueue } = getStartingNodes(nonDirectedGraph, endingNodeId)
|
||||
|
||||
/*** BFS to traverse from Starting Nodes to Ending Node ***/
|
||||
const reactFlowNodes = await buildLangchain(
|
||||
startingNodeIds,
|
||||
parsedFlowData.nodes,
|
||||
nodes,
|
||||
graph,
|
||||
depthQueue,
|
||||
this.nodesPool.componentNodes
|
||||
this.nodesPool.componentNodes,
|
||||
incomingInput.question
|
||||
)
|
||||
|
||||
const nodeToExecute = reactFlowNodes.find((node: IReactFlowNode) => node.id === endingNodeId)
|
||||
if (!nodeToExecute) return res.status(404).send(`Node ${endingNodeId} not found`)
|
||||
|
||||
nodeToExecuteData = nodeToExecute.data
|
||||
const reactFlowNodeData: INodeData = resolveVariables(nodeToExecute.data, reactFlowNodes, incomingInput.question)
|
||||
nodeToExecuteData = reactFlowNodeData
|
||||
|
||||
this.chatflowPool.add(chatflowid, nodeToExecuteData)
|
||||
}
|
||||
|
||||
@@ -8,10 +8,14 @@ import {
|
||||
INodeDirectedGraph,
|
||||
INodeQueue,
|
||||
IReactFlowEdge,
|
||||
IReactFlowNode
|
||||
IReactFlowNode,
|
||||
IVariableDict,
|
||||
INodeData
|
||||
} from '../Interface'
|
||||
import { cloneDeep, get } from 'lodash'
|
||||
import { ICommonObject, INodeData } from 'flowise-components'
|
||||
import { ICommonObject } from 'flowise-components'
|
||||
|
||||
const QUESTION_VAR_PREFIX = 'question'
|
||||
|
||||
/**
|
||||
* Returns the home folder path of the user if
|
||||
@@ -166,13 +170,15 @@ export const getEndingNode = (nodeDependencies: INodeDependencies, graph: INodeD
|
||||
* @param {INodeDirectedGraph} graph
|
||||
* @param {IDepthQueue} depthQueue
|
||||
* @param {IComponentNodes} componentNodes
|
||||
* @param {string} question
|
||||
*/
|
||||
export const buildLangchain = async (
|
||||
startingNodeIds: string[],
|
||||
reactFlowNodes: IReactFlowNode[],
|
||||
graph: INodeDirectedGraph,
|
||||
depthQueue: IDepthQueue,
|
||||
componentNodes: IComponentNodes
|
||||
componentNodes: IComponentNodes,
|
||||
question: string
|
||||
) => {
|
||||
const flowNodes = cloneDeep(reactFlowNodes)
|
||||
|
||||
@@ -200,9 +206,9 @@ export const buildLangchain = async (
|
||||
const nodeModule = await import(nodeInstanceFilePath)
|
||||
const newNodeInstance = new nodeModule.nodeClass()
|
||||
|
||||
const reactFlowNodeData: INodeData = resolveVariables(reactFlowNode.data, flowNodes)
|
||||
const reactFlowNodeData: INodeData = resolveVariables(reactFlowNode.data, flowNodes, question)
|
||||
|
||||
flowNodes[nodeIndex].data.instance = await newNodeInstance.init(reactFlowNodeData)
|
||||
flowNodes[nodeIndex].data.instance = await newNodeInstance.init(reactFlowNodeData, question)
|
||||
} catch (e: any) {
|
||||
console.error(e)
|
||||
throw new Error(e)
|
||||
@@ -247,11 +253,14 @@ export const buildLangchain = async (
|
||||
* Get variable value from outputResponses.output
|
||||
* @param {string} paramValue
|
||||
* @param {IReactFlowNode[]} reactFlowNodes
|
||||
* @param {string} question
|
||||
* @param {boolean} isAcceptVariable
|
||||
* @returns {string}
|
||||
*/
|
||||
export const getVariableValue = (paramValue: string, reactFlowNodes: IReactFlowNode[]) => {
|
||||
export const getVariableValue = (paramValue: string, reactFlowNodes: IReactFlowNode[], question: string, isAcceptVariable = false) => {
|
||||
let returnVal = paramValue
|
||||
const variableStack = []
|
||||
const variableDict = {} as IVariableDict
|
||||
let startIdx = 0
|
||||
const endIdx = returnVal.length - 1
|
||||
|
||||
@@ -269,17 +278,36 @@ export const getVariableValue = (paramValue: string, reactFlowNodes: IReactFlowN
|
||||
const variableEndIdx = startIdx
|
||||
const variableFullPath = returnVal.substring(variableStartIdx, variableEndIdx)
|
||||
|
||||
if (isAcceptVariable && variableFullPath === QUESTION_VAR_PREFIX) {
|
||||
variableDict[`{{${variableFullPath}}}`] = question
|
||||
}
|
||||
|
||||
// Split by first occurence of '.' to get just nodeId
|
||||
const [variableNodeId, _] = variableFullPath.split('.')
|
||||
const executedNode = reactFlowNodes.find((nd) => nd.id === variableNodeId)
|
||||
if (executedNode) {
|
||||
const variableInstance = get(executedNode.data, 'instance')
|
||||
returnVal = variableInstance
|
||||
const variableValue = get(executedNode.data, 'instance')
|
||||
if (isAcceptVariable) {
|
||||
variableDict[`{{${variableFullPath}}}`] = variableValue
|
||||
} else {
|
||||
returnVal = variableValue
|
||||
}
|
||||
}
|
||||
variableStack.pop()
|
||||
}
|
||||
startIdx += 1
|
||||
}
|
||||
|
||||
if (isAcceptVariable) {
|
||||
const variablePaths = Object.keys(variableDict)
|
||||
variablePaths.sort() // Sort by length of variable path because longer path could possibly contains nested variable
|
||||
variablePaths.forEach((path) => {
|
||||
const variableValue = variableDict[path]
|
||||
// Replace all occurence
|
||||
returnVal = returnVal.split(path).join(variableValue)
|
||||
})
|
||||
return returnVal
|
||||
}
|
||||
return returnVal
|
||||
}
|
||||
|
||||
@@ -287,25 +315,26 @@ export const getVariableValue = (paramValue: string, reactFlowNodes: IReactFlowN
|
||||
* Loop through each inputs and resolve variable if neccessary
|
||||
* @param {INodeData} reactFlowNodeData
|
||||
* @param {IReactFlowNode[]} reactFlowNodes
|
||||
* @param {string} question
|
||||
* @returns {INodeData}
|
||||
*/
|
||||
export const resolveVariables = (reactFlowNodeData: INodeData, reactFlowNodes: IReactFlowNode[]): INodeData => {
|
||||
export const resolveVariables = (reactFlowNodeData: INodeData, reactFlowNodes: IReactFlowNode[], question: string): INodeData => {
|
||||
const flowNodeData = cloneDeep(reactFlowNodeData)
|
||||
const types = 'inputs'
|
||||
|
||||
const getParamValues = (paramsObj: ICommonObject) => {
|
||||
for (const key in paramsObj) {
|
||||
const paramValue: string = paramsObj[key]
|
||||
|
||||
if (Array.isArray(paramValue)) {
|
||||
const resolvedInstances = []
|
||||
for (const param of paramValue) {
|
||||
const resolvedInstance = getVariableValue(param, reactFlowNodes)
|
||||
const resolvedInstance = getVariableValue(param, reactFlowNodes, question)
|
||||
resolvedInstances.push(resolvedInstance)
|
||||
}
|
||||
paramsObj[key] = resolvedInstances
|
||||
} else {
|
||||
const resolvedInstance = getVariableValue(paramValue, reactFlowNodes)
|
||||
const isAcceptVariable = reactFlowNodeData.inputParams.find((param) => param.name === key)?.acceptVariable ?? false
|
||||
const resolvedInstance = getVariableValue(paramValue, reactFlowNodes, question, isAcceptVariable)
|
||||
paramsObj[key] = resolvedInstance
|
||||
}
|
||||
}
|
||||
@@ -317,3 +346,24 @@ export const resolveVariables = (reactFlowNodeData: INodeData, reactFlowNodes: I
|
||||
|
||||
return flowNodeData
|
||||
}
|
||||
|
||||
/**
|
||||
* Rebuild flow if LLMChain has dependency on other chains
|
||||
* User Question => Prompt_0 => LLMChain_0 => Prompt-1 => LLMChain_1
|
||||
* @param {IReactFlowNode[]} nodes
|
||||
* @param {INodeData} nodeData
|
||||
* @returns {boolean}
|
||||
*/
|
||||
export const checkIfFlowNeedToRebuild = (nodes: IReactFlowNode[], nodeData: INodeData) => {
|
||||
if (nodeData.name !== 'llmChain') return false
|
||||
|
||||
const node = nodes.find((nd) => nd.id === nodeData.id)
|
||||
if (!node) throw new Error(`Node ${nodeData.id} not found`)
|
||||
|
||||
const inputs = node.data.inputs
|
||||
for (const key in inputs) {
|
||||
const isInputAcceptVariable = node.data.inputParams.find((param) => param.name === key)?.acceptVariable || false
|
||||
if (isInputAcceptVariable && inputs[key].includes('{{') && inputs[key].includes('}}')) return true
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user