Merge branch 'main' into feature/RateLimit

packages/components/nodes/chains/SqlDatabaseChain/SqlDatabaseChain.ts

@@ -1,14 +1,34 @@
 import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { SqlDatabaseChain, SqlDatabaseChainInput } from 'langchain/chains/sql_db'
-import { getBaseClasses } from '../../../src/utils'
+import { getBaseClasses, getInputVariables } from '../../../src/utils'
 import { DataSource } from 'typeorm'
 import { SqlDatabase } from 'langchain/sql_db'
 import { BaseLanguageModel } from 'langchain/base_language'
+import { PromptTemplate, PromptTemplateInput } from 'langchain/prompts'
 import { ConsoleCallbackHandler, CustomChainHandler } from '../../../src/handler'
 import { DataSourceOptions } from 'typeorm/data-source'
 
 type DatabaseType = 'sqlite' | 'postgres' | 'mssql' | 'mysql'
 
+const defaultPrompt = `Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.
+
+Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.
+
+Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
+
+Use the following format:
+
+Question: "Question here"
+SQLQuery: "SQL Query to run"
+SQLResult: "Result of the SQLQuery"
+Answer: "Final answer here"
+
+Only use the tables listed below.
+
+{table_info}
+
+Question: {input}`
+
 class SqlDatabaseChain_Chains implements INode {
     label: string
     name: string
@@ -23,7 +43,7 @@ class SqlDatabaseChain_Chains implements INode {
     constructor() {
         this.label = 'Sql Database Chain'
         this.name = 'sqlDatabaseChain'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'SqlDatabaseChain'
         this.icon = 'sqlchain.svg'
         this.category = 'Chains'
@@ -64,6 +84,19 @@ class SqlDatabaseChain_Chains implements INode {
                 name: 'url',
                 type: 'string',
                 placeholder: '1270.0.0.1:5432/chinook'
+            },
+            {
+                label: 'Custom Prompt',
+                name: 'customPrompt',
+                type: 'string',
+                description:
+                    'You can provide custom prompt to the chain. This will override the existing default prompt used. See <a target="_blank" href="https://python.langchain.com/docs/integrations/tools/sqlite#customize-prompt">guide</a>',
+                warning:
+                    'Prompt must include 3 input variables: {input}, {dialect}, {table_info}. You can refer to official guide from description above',
+                rows: 4,
+                placeholder: defaultPrompt,
+                additionalParams: true,
+                optional: true
             }
         ]
     }
@@ -72,8 +105,9 @@ class SqlDatabaseChain_Chains implements INode {
         const databaseType = nodeData.inputs?.database as DatabaseType
         const model = nodeData.inputs?.model as BaseLanguageModel
         const url = nodeData.inputs?.url
+        const customPrompt = nodeData.inputs?.customPrompt as string
 
-        const chain = await getSQLDBChain(databaseType, url, model)
+        const chain = await getSQLDBChain(databaseType, url, model, customPrompt)
         return chain
     }
 
@@ -81,8 +115,9 @@ class SqlDatabaseChain_Chains implements INode {
         const databaseType = nodeData.inputs?.database as DatabaseType
         const model = nodeData.inputs?.model as BaseLanguageModel
         const url = nodeData.inputs?.url
+        const customPrompt = nodeData.inputs?.customPrompt as string
 
-        const chain = await getSQLDBChain(databaseType, url, model)
+        const chain = await getSQLDBChain(databaseType, url, model, customPrompt)
         const loggerHandler = new ConsoleCallbackHandler(options.logger)
 
         if (options.socketIO && options.socketIOClientId) {
@@ -96,7 +131,7 @@ class SqlDatabaseChain_Chains implements INode {
     }
 }
 
-const getSQLDBChain = async (databaseType: DatabaseType, url: string, llm: BaseLanguageModel) => {
+const getSQLDBChain = async (databaseType: DatabaseType, url: string, llm: BaseLanguageModel, customPrompt?: string) => {
     const datasource = new DataSource(
         databaseType === 'sqlite'
             ? {
@@ -119,6 +154,14 @@ const getSQLDBChain = async (databaseType: DatabaseType, url: string, llm: BaseL
         verbose: process.env.DEBUG === 'true' ? true : false
     }
 
+    if (customPrompt) {
+        const options: PromptTemplateInput = {
+            template: customPrompt,
+            inputVariables: getInputVariables(customPrompt)
+        }
+        obj.prompt = new PromptTemplate(options)
+    }
+
     const chain = new SqlDatabaseChain(obj)
     return chain
 }

packages/components/nodes/chatmodels/ChatOpenAIFineTuned/ChatOpenAIFineTuned.ts

@@ -0,0 +1,149 @@
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { ChatOpenAI, OpenAIChatInput } from 'langchain/chat_models/openai'
+
+class ChatOpenAIFineTuned_ChatModels implements INode {
+    label: string
+    name: string
+    version: number
+    type: string
+    icon: string
+    category: string
+    description: string
+    baseClasses: string[]
+    credential: INodeParams
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'ChatOpenAI Fine-Tuned'
+        this.name = 'chatOpenAIFineTuned'
+        this.version = 1.0
+        this.type = 'ChatOpenAI-FineTuned'
+        this.icon = 'openai.png'
+        this.category = 'Chat Models'
+        this.description = 'Wrapper around fine-tuned OpenAI LLM that use the Chat endpoint'
+        this.baseClasses = [this.type, ...getBaseClasses(ChatOpenAI)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['openAIApi']
+        }
+        this.inputs = [
+            {
+                label: 'Model Name',
+                name: 'modelName',
+                type: 'string',
+                placeholder: 'ft:gpt-3.5-turbo:my-org:custom_suffix:id'
+            },
+            {
+                label: 'Temperature',
+                name: 'temperature',
+                type: 'number',
+                step: 0.1,
+                default: 0.9,
+                optional: true
+            },
+            {
+                label: 'Max Tokens',
+                name: 'maxTokens',
+                type: 'number',
+                step: 1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Top Probability',
+                name: 'topP',
+                type: 'number',
+                step: 0.1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Frequency Penalty',
+                name: 'frequencyPenalty',
+                type: 'number',
+                step: 0.1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Presence Penalty',
+                name: 'presencePenalty',
+                type: 'number',
+                step: 0.1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Timeout',
+                name: 'timeout',
+                type: 'number',
+                step: 1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'BasePath',
+                name: 'basepath',
+                type: 'string',
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'BaseOptions',
+                name: 'baseOptions',
+                type: 'json',
+                optional: true,
+                additionalParams: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const temperature = nodeData.inputs?.temperature as string
+        const modelName = nodeData.inputs?.modelName as string
+        const maxTokens = nodeData.inputs?.maxTokens as string
+        const topP = nodeData.inputs?.topP as string
+        const frequencyPenalty = nodeData.inputs?.frequencyPenalty as string
+        const presencePenalty = nodeData.inputs?.presencePenalty as string
+        const timeout = nodeData.inputs?.timeout as string
+        const streaming = nodeData.inputs?.streaming as boolean
+        const basePath = nodeData.inputs?.basepath as string
+        const baseOptions = nodeData.inputs?.baseOptions
+
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
+
+        const obj: Partial<OpenAIChatInput> & { openAIApiKey?: string } = {
+            temperature: parseFloat(temperature),
+            modelName,
+            openAIApiKey,
+            streaming: streaming ?? true
+        }
+
+        if (maxTokens) obj.maxTokens = parseInt(maxTokens, 10)
+        if (topP) obj.topP = parseFloat(topP)
+        if (frequencyPenalty) obj.frequencyPenalty = parseFloat(frequencyPenalty)
+        if (presencePenalty) obj.presencePenalty = parseFloat(presencePenalty)
+        if (timeout) obj.timeout = parseInt(timeout, 10)
+
+        let parsedBaseOptions: any | undefined = undefined
+
+        if (baseOptions) {
+            try {
+                parsedBaseOptions = typeof baseOptions === 'object' ? baseOptions : JSON.parse(baseOptions)
+            } catch (exception) {
+                throw new Error("Invalid JSON in the ChatOpenAI's BaseOptions: " + exception)
+            }
+        }
+        const model = new ChatOpenAI(obj, {
+            basePath,
+            baseOptions: parsedBaseOptions
+        })
+        return model
+    }
+}
+
+module.exports = { nodeClass: ChatOpenAIFineTuned_ChatModels }

packages/components/nodes/llms/OpenAI/OpenAI.ts

@@ -125,6 +125,13 @@ class OpenAI_LLMs implements INode {
                 type: 'string',
                 optional: true,
                 additionalParams: true
+            },
+            {
+                label: 'BaseOptions',
+                name: 'baseOptions',
+                type: 'json',
+                optional: true,
+                additionalParams: true
             }
         ]
     }
@@ -141,6 +148,7 @@ class OpenAI_LLMs implements INode {
         const bestOf = nodeData.inputs?.bestOf as string
         const streaming = nodeData.inputs?.streaming as boolean
         const basePath = nodeData.inputs?.basepath as string
+        const baseOptions = nodeData.inputs?.baseOptions
 
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, nodeData)
@@ -160,8 +168,19 @@ class OpenAI_LLMs implements INode {
         if (batchSize) obj.batchSize = parseInt(batchSize, 10)
         if (bestOf) obj.bestOf = parseInt(bestOf, 10)
 
+        let parsedBaseOptions: any | undefined = undefined
+
+        if (baseOptions) {
+            try {
+                parsedBaseOptions = typeof baseOptions === 'object' ? baseOptions : JSON.parse(baseOptions)
+            } catch (exception) {
+                throw new Error("Invalid JSON in the OpenAI's BaseOptions: " + exception)
+            }
+        }
+
         const model = new OpenAI(obj, {
-            basePath
+            basePath,
+            baseOptions: parsedBaseOptions
         })
         return model
     }

packages/server/marketplaces/chatflows/SQL DB Chain.json

@@ -157,17 +157,17 @@
         },
         {
             "width": 300,
-            "height": 423,
+            "height": 475,
             "id": "sqlDatabaseChain_0",
             "position": {
-                "x": 1229.0092429246013,
-                "y": 231.59431102290245
+                "x": 1206.5244299447634,
+                "y": 201.04431101230608
             },
             "type": "customNode",
             "data": {
                 "id": "sqlDatabaseChain_0",
                 "label": "Sql Database Chain",
-                "version": 1,
+                "version": 2,
                 "name": "sqlDatabaseChain",
                 "type": "SqlDatabaseChain",
                 "baseClasses": ["SqlDatabaseChain", "BaseChain", "Runnable"],
@@ -205,6 +205,18 @@
                         "type": "string",
                         "placeholder": "1270.0.0.1:5432/chinook",
                         "id": "sqlDatabaseChain_0-input-url-string"
+                    },
+                    {
+                        "label": "Custom Prompt",
+                        "name": "customPrompt",
+                        "type": "string",
+                        "description": "You can provide custom prompt to the chain. This will override the existing default prompt used. See <a target=\"_blank\" href=\"https://python.langchain.com/docs/integrations/tools/sqlite#customize-prompt\">guide</a>",
+                        "warning": "Prompt must include 3 input variables: {input}, {dialect}, {table_info}. You can refer to official guide from description above",
+                        "rows": 4,
+                        "placeholder": "Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.\n\nNever query for all the columns from a specific table, only ask for a the few relevant columns given the question.\n\nPay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.\n\nUse the following format:\n\nQuestion: \"Question here\"\nSQLQuery: \"SQL Query to run\"\nSQLResult: \"Result of the SQLQuery\"\nAnswer: \"Final answer here\"\n\nOnly use the tables listed below.\n\n{table_info}\n\nQuestion: {input}",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "sqlDatabaseChain_0-input-customPrompt-string"
                     }
                 ],
                 "inputAnchors": [
@@ -218,7 +230,8 @@
                 "inputs": {
                     "model": "{{chatOpenAI_0.data.instance}}",
                     "database": "sqlite",
-                    "url": ""
+                    "url": "",
+                    "customPrompt": ""
                 },
                 "outputAnchors": [
                     {
@@ -233,8 +246,8 @@
             },
             "selected": false,
             "positionAbsolute": {
-                "x": 1229.0092429246013,
-                "y": 231.59431102290245
+                "x": 1206.5244299447634,
+                "y": 201.04431101230608
             },
             "dragging": false
         }