Feature/seq agents (#2798)

* update build functions * sequential agents * update langchain to 0.2, added sequential agent nodes * add marketplace templates * update howto wordings * Merge branch 'main' into feature/Seq-Agents # Conflicts: # pnpm-lock.yaml * update deprecated functions and add new sequential nodes * add marketplace templates * update marketplace templates, add structured output to llm node * add multi agents template * update llm node with bindmodels * update cypress version * update templates sticky note wordings * update tool node to include human in loop action * update structured outputs error from models * update cohere package to resolve google genai pipeThrough bug * update mistral package version, added message reconstruction before invoke seq agent * add HITL to agent * update state messages restructuring * update load and split methods for s3 directory
2026-06-28 21:00:58 +03:00 · 2024-07-22 17:46:14 +01:00
parent 34d0e4302c
commit bca4de0c63
152 changed files with 55307 additions and 35236 deletions
@@ -1,10 +1,9 @@
 import { BaseCache } from '@langchain/core/caches'
-import { BaseChatModelParams } from '@langchain/core/language_models/chat_models'
-import { BaseBedrockInput } from '@langchain/community/dist/utils/bedrock'
 import { ICommonObject, IMultiModalOption, INode, INodeData, INodeOptionsValue, INodeParams } from '../../../src/Interface'
 import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { BedrockChat } from './FlowiseAWSChatBedrock'
 import { getModels, getRegions, MODEL_TYPE } from '../../../src/modelLoader'
+import { BedrockChatFields } from '@langchain/community/chat_models/bedrock'

 /**
 * @author Michael Connor <mlconnor@yahoo.com>
@@ -116,7 +115,7 @@ class AWSChatBedrock_ChatModels implements INode {
        const cache = nodeData.inputs?.cache as BaseCache
        const streaming = nodeData.inputs?.streaming as boolean

-        const obj: BaseBedrockInput & BaseChatModelParams = {
+        const obj: BedrockChatFields = {
            region: iRegion,
            model: customModel ? customModel : iModel,
            maxTokens: parseInt(iMax_tokens_to_sample, 10),
@@ -154,7 +153,7 @@ class AWSChatBedrock_ChatModels implements INode {
        }

        const amazonBedrock = new BedrockChat(nodeData.id, obj)
-        if (obj.model.includes('anthropic.claude-3')) amazonBedrock.setMultiModalOption(multiModalOption)
+        if (obj.model?.includes('anthropic.claude-3')) amazonBedrock.setMultiModalOption(multiModalOption)
        return amazonBedrock
    }
 }
@@ -1,6 +1,4 @@
-import { BaseChatModelParams } from '@langchain/core/language_models/chat_models'
-import { BedrockChat as LCBedrockChat } from '@langchain/community/chat_models/bedrock'
-import { BaseBedrockInput } from '@langchain/community/dist/utils/bedrock'
+import { BedrockChatFields, BedrockChat as LCBedrockChat } from '@langchain/community/chat_models/bedrock'
 import { IVisionChatModal, IMultiModalOption } from '../../../src'

 export class BedrockChat extends LCBedrockChat implements IVisionChatModal {
@@ -9,7 +7,7 @@ export class BedrockChat extends LCBedrockChat implements IVisionChatModal {
    multiModalOption: IMultiModalOption
    id: string

-    constructor(id: string, fields: BaseBedrockInput & BaseChatModelParams) {
+    constructor(id: string, fields: BedrockChatFields) {
        super(fields)
        this.id = id
        this.configuredModel = fields?.model || ''
@@ -17,8 +15,8 @@ export class BedrockChat extends LCBedrockChat implements IVisionChatModal {
    }

    revertToOriginalModel(): void {
-        super.model = this.configuredModel
-        super.maxTokens = this.configuredMaxToken
+        this.model = this.configuredModel
+        this.maxTokens = this.configuredMaxToken
    }

    setMultiModalOption(multiModalOption: IMultiModalOption): void {
@@ -27,8 +25,8 @@ export class BedrockChat extends LCBedrockChat implements IVisionChatModal {

    setVisionModel(): void {
        if (!this.model.startsWith('claude-3')) {
-            super.model = 'anthropic.claude-3-haiku-20240307-v1:0'
-            super.maxTokens = this.configuredMaxToken ? this.configuredMaxToken : 1024
+            this.model = 'anthropic.claude-3-haiku-20240307-v1:0'
+            this.maxTokens = this.configuredMaxToken ? this.configuredMaxToken : 1024
        }
    }
 }
@@ -1,57 +0,0 @@
-import { BaseCache } from '@langchain/core/caches'
-import { NIBittensorChatModel, BittensorInput } from 'langchain/experimental/chat_models/bittensor'
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
-
-class Bittensor_ChatModels implements INode {
-    label: string
-    name: string
-    version: number
-    type: string
-    icon: string
-    category: string
-    description: string
-    baseClasses: string[]
-    inputs: INodeParams[]
-
-    constructor() {
-        this.label = 'NIBittensorChat'
-        this.name = 'NIBittensorChatModel'
-        this.version = 2.0
-        this.type = 'BittensorChat'
-        this.icon = 'NIBittensor.svg'
-        this.category = 'Chat Models'
-        this.description = 'Wrapper around Bittensor subnet 1 large language models'
-        this.baseClasses = [this.type, ...getBaseClasses(NIBittensorChatModel)]
-        this.inputs = [
-            {
-                label: 'Cache',
-                name: 'cache',
-                type: 'BaseCache',
-                optional: true
-            },
-            {
-                label: 'System prompt',
-                name: 'system_prompt',
-                type: 'string',
-                additionalParams: true,
-                optional: true
-            }
-        ]
-    }
-
-    async init(nodeData: INodeData, _: string): Promise<any> {
-        const system_prompt = nodeData.inputs?.system_prompt as string
-        const cache = nodeData.inputs?.cache as BaseCache
-
-        const obj: Partial<BittensorInput> = {
-            systemPrompt: system_prompt
-        }
-        if (cache) obj.cache = cache
-
-        const model = new NIBittensorChatModel(obj)
-        return model
-    }
-}
-
-module.exports = { nodeClass: Bittensor_ChatModels }
@@ -1 +0,0 @@
-<svg width="32" height="32" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M18.64 25.698V29H8l1.61-6.25a9.81 9.81 0 0 0-.045-4.5C9.027 15.808 8 15.394 8 10.824c.01-2.35.916-4.601 2.517-6.256C12.12 2.913 14.285 1.989 16.54 2c2.254.01 4.412.955 5.999 2.625 1.587 1.67 2.472 3.93 2.462 6.28V12l2 4h-2v4.208a3.821 3.821 0 0 1-1.08 2.373 3.531 3.531 0 0 1-2.306 1.054c-.165.01-.375.004-.606-.012-1.242-.085-2.367.83-2.367 2.075Z" fill="#000" stroke="#000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/><path d="M21 13h-2l-1-2m3-1-1-2h-4m-3 1 2 4m-1 6 3-3h4" stroke="#fff" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/></svg>
@@ -16,8 +16,8 @@ export class ChatAnthropic extends LangchainChatAnthropic implements IVisionChat
    }

    revertToOriginalModel(): void {
-        super.modelName = this.configuredModel
-        super.maxTokens = this.configuredMaxToken
+        this.modelName = this.configuredModel
+        this.maxTokens = this.configuredMaxToken
    }

    setMultiModalOption(multiModalOption: IMultiModalOption): void {
@@ -26,8 +26,8 @@ export class ChatAnthropic extends LangchainChatAnthropic implements IVisionChat

    setVisionModel(): void {
        if (!this.modelName.startsWith('claude-3')) {
-            super.modelName = 'claude-3-haiku-20240307'
-            super.maxTokens = this.configuredMaxToken ? this.configuredMaxToken : 2048
+            this.modelName = 'claude-3-haiku-20240307'
+            this.maxTokens = this.configuredMaxToken ? this.configuredMaxToken : 2048
        }
    }
 }
@@ -1,8 +1,8 @@
-import { BaseMessage, AIMessage, AIMessageChunk, isBaseMessage, ChatMessage, MessageContent } from '@langchain/core/messages'
+import { BaseMessage, AIMessage, AIMessageChunk, isBaseMessage, ChatMessage, MessageContentComplex } from '@langchain/core/messages'
 import { CallbackManagerForLLMRun } from '@langchain/core/callbacks/manager'
 import { BaseChatModel, type BaseChatModelParams } from '@langchain/core/language_models/chat_models'
 import { ChatGeneration, ChatGenerationChunk, ChatResult } from '@langchain/core/outputs'
-import { ToolCall } from '@langchain/core/messages/tool'
+import { ToolCallChunk } from '@langchain/core/messages/tool'
 import { NewTokenIndices } from '@langchain/core/callbacks/base'
 import {
    EnhancedGenerateContentResponse,
@@ -12,11 +12,19 @@ import {
    GenerativeModel,
    GoogleGenerativeAI as GenerativeAI
 } from '@google/generative-ai'
-import type { SafetySetting } from '@google/generative-ai'
+import type {
+    FunctionCallPart,
+    FunctionResponsePart,
+    SafetySetting,
+    UsageMetadata,
+    FunctionDeclarationsTool as GoogleGenerativeAIFunctionDeclarationsTool,
+    GenerateContentRequest
+} from '@google/generative-ai'
 import { ICommonObject, IMultiModalOption, IVisionChatModal } from '../../../src'
 import { StructuredToolInterface } from '@langchain/core/tools'
 import { isStructuredTool } from '@langchain/core/utils/function_calling'
 import { zodToJsonSchema } from 'zod-to-json-schema'
+import { BaseLanguageModelCallOptions } from '@langchain/core/language_models/base'

 interface TokenUsage {
    completionTokens?: number
@@ -24,7 +32,17 @@ interface TokenUsage {
    totalTokens?: number
 }

-export interface GoogleGenerativeAIChatInput extends BaseChatModelParams {
+interface GoogleGenerativeAIChatCallOptions extends BaseLanguageModelCallOptions {
+    tools?: StructuredToolInterface[] | GoogleGenerativeAIFunctionDeclarationsTool[]
+    /**
+     * Whether or not to include usage data, like token counts
+     * in the streamed response chunks.
+     * @default true
+     */
+    streamUsage?: boolean
+}
+
+export interface GoogleGenerativeAIChatInput extends BaseChatModelParams, Pick<GoogleGenerativeAIChatCallOptions, 'streamUsage'> {
    modelName?: string
    model?: string
    temperature?: number
@@ -34,10 +52,15 @@ export interface GoogleGenerativeAIChatInput extends BaseChatModelParams {
    stopSequences?: string[]
    safetySettings?: SafetySetting[]
    apiKey?: string
+    apiVersion?: string
+    baseUrl?: string
    streaming?: boolean
 }

-class LangchainChatGoogleGenerativeAI extends BaseChatModel implements GoogleGenerativeAIChatInput {
+class LangchainChatGoogleGenerativeAI
+    extends BaseChatModel<GoogleGenerativeAIChatCallOptions, AIMessageChunk>
+    implements GoogleGenerativeAIChatInput
+{
    modelName = 'gemini-pro'

    temperature?: number
@@ -56,6 +79,8 @@ class LangchainChatGoogleGenerativeAI extends BaseChatModel implements GoogleGen

    streaming = false

+    streamUsage = true
+
    private client: GenerativeModel

    get _isMultimodalModel() {
@@ -114,6 +139,8 @@ class LangchainChatGoogleGenerativeAI extends BaseChatModel implements GoogleGen

        this.streaming = fields?.streaming ?? this.streaming

+        this.streamUsage = fields?.streamUsage ?? this.streamUsage
+
        this.getClient()
    }

@@ -146,6 +173,18 @@ class LangchainChatGoogleGenerativeAI extends BaseChatModel implements GoogleGen
        return this.bind({ tools: convertToGeminiTools(tools), ...kwargs })
    }

+    invocationParams(options?: this['ParsedCallOptions']): Omit<GenerateContentRequest, 'contents'> {
+        const tools = options?.tools as GoogleGenerativeAIFunctionDeclarationsTool[] | StructuredToolInterface[] | undefined
+        if (Array.isArray(tools) && !tools.some((t: any) => !('lc_namespace' in t))) {
+            return {
+                tools: convertToGeminiTools(options?.tools as StructuredToolInterface[]) as any
+            }
+        }
+        return {
+            tools: options?.tools as GoogleGenerativeAIFunctionDeclarationsTool[] | undefined
+        }
+    }
+
    convertFunctionResponse(prompts: Content[]) {
        for (let i = 0; i < prompts.length; i += 1) {
            if (prompts[i].role === 'function') {
@@ -178,7 +217,7 @@ class LangchainChatGoogleGenerativeAI extends BaseChatModel implements GoogleGen
        this.convertFunctionResponse(prompt)

        if (tools.length > 0) {
-            this.getClient(tools)
+            this.getClient(tools as Tool[])
        } else {
            this.getClient()
        }
@@ -214,6 +253,7 @@ class LangchainChatGoogleGenerativeAI extends BaseChatModel implements GoogleGen
            const tokenUsage: TokenUsage = {}
            const stream = this._streamResponseChunks(messages, options, runManager)
            const finalChunks: Record<number, ChatGenerationChunk> = {}
+
            for await (const chunk of stream) {
                const index = (chunk.generationInfo as NewTokenIndices)?.completion ?? 0
                if (finalChunks[index] === undefined) {
@@ -239,45 +279,62 @@ class LangchainChatGoogleGenerativeAI extends BaseChatModel implements GoogleGen
        let prompt = convertBaseMessagesToContent(messages, this._isMultimodalModel)
        prompt = checkIfEmptyContentAndSameRole(prompt)

-        //@ts-ignore
-        if (options.tools !== undefined && options.tools.length > 0) {
-            const result = await this._generateNonStreaming(prompt, options, runManager)
-            const generationMessage = result.generations[0].message as AIMessage
-            if (generationMessage === undefined) {
-                throw new Error('Could not parse Groq output.')
-            }
-            const toolCallChunks = generationMessage.tool_calls?.map((toolCall, i) => ({
-                name: toolCall.name,
-                args: JSON.stringify(toolCall.args),
-                id: toolCall.id,
-                index: i
-            }))
-            yield new ChatGenerationChunk({
-                message: new AIMessageChunk({
-                    content: generationMessage.content,
-                    additional_kwargs: generationMessage.additional_kwargs,
-                    tool_call_chunks: toolCallChunks
-                }),
-                text: generationMessage.tool_calls?.length ? '' : (generationMessage.content as string)
-            })
+        const parameters = this.invocationParams(options)
+        const request = {
+            ...parameters,
+            contents: prompt
+        }
+
+        const tools = options.tools ?? []
+        if (tools.length > 0) {
+            this.getClient(tools as Tool[])
        } else {
-            const stream = await this.caller.callWithOptions({ signal: options?.signal }, async () => {
-                this.getClient()
-                const { stream } = await this.client.generateContentStream({
-                    contents: prompt
-                })
-                return stream
-            })
+            this.getClient()
+        }

-            for await (const response of stream) {
-                const chunk = convertResponseContentToChatGenerationChunk(response)
-                if (!chunk) {
-                    continue
+        const stream = await this.caller.callWithOptions({ signal: options?.signal }, async () => {
+            const { stream } = await this.client.generateContentStream(request)
+            return stream
+        })
+
+        let usageMetadata: UsageMetadata | ICommonObject | undefined
+        let index = 0
+        for await (const response of stream) {
+            if ('usageMetadata' in response && this.streamUsage !== false && options.streamUsage !== false) {
+                const genAIUsageMetadata = response.usageMetadata as {
+                    promptTokenCount: number
+                    candidatesTokenCount: number
+                    totalTokenCount: number
+                }
+                if (!usageMetadata) {
+                    usageMetadata = {
+                        input_tokens: genAIUsageMetadata.promptTokenCount,
+                        output_tokens: genAIUsageMetadata.candidatesTokenCount,
+                        total_tokens: genAIUsageMetadata.totalTokenCount
+                    }
+                } else {
+                    // Under the hood, LangChain combines the prompt tokens. Google returns the updated
+                    // total each time, so we need to find the difference between the tokens.
+                    const outputTokenDiff = genAIUsageMetadata.candidatesTokenCount - (usageMetadata as ICommonObject).output_tokens
+                    usageMetadata = {
+                        input_tokens: 0,
+                        output_tokens: outputTokenDiff,
+                        total_tokens: outputTokenDiff
+                    }
                }
-
-                yield chunk
-                await runManager?.handleLLMNewToken(chunk.text ?? '')
            }
+
+            const chunk = convertResponseContentToChatGenerationChunk(response, {
+                usageMetadata: usageMetadata as UsageMetadata,
+                index
+            })
+            index += 1
+            if (!chunk) {
+                continue
+            }
+
+            yield chunk
+            await runManager?.handleLLMNewToken(chunk.text ?? '')
        }
    }
 }
@@ -296,8 +353,8 @@ export class ChatGoogleGenerativeAI extends LangchainChatGoogleGenerativeAI impl
    }

    revertToOriginalModel(): void {
-        super.modelName = this.configuredModel
-        super.maxOutputTokens = this.configuredMaxToken
+        this.modelName = this.configuredModel
+        this.maxOutputTokens = this.configuredMaxToken
    }

    setMultiModalOption(multiModalOption: IMultiModalOption): void {
@@ -306,12 +363,25 @@ export class ChatGoogleGenerativeAI extends LangchainChatGoogleGenerativeAI impl

    setVisionModel(): void {
        if (this.modelName !== 'gemini-pro-vision' && this.modelName !== 'gemini-1.5-pro-latest') {
-            super.modelName = 'gemini-1.5-pro-latest'
-            super.maxOutputTokens = this.configuredMaxToken ? this.configuredMaxToken : 8192
+            this.modelName = 'gemini-1.5-pro-latest'
+            this.maxOutputTokens = this.configuredMaxToken ? this.configuredMaxToken : 8192
        }
    }
 }

+function messageContentMedia(content: MessageContentComplex): Part {
+    if ('mimeType' in content && 'data' in content) {
+        return {
+            inlineData: {
+                mimeType: content.mimeType,
+                data: content.data
+            }
+        }
+    }
+
+    throw new Error('Invalid media content')
+}
+
 function getMessageAuthor(message: BaseMessage) {
    const type = message._getType()
    if (ChatMessage.isInstance(message)) {
@@ -336,69 +406,88 @@ function convertAuthorToRole(author: string) {
        case 'tool':
            return 'function'
        default:
-            // Instead of throwing, we return model
+            // Instead of throwing, we return model (Needed for Multi Agent)
            // throw new Error(`Unknown / unsupported author: ${author}`)
            return 'model'
    }
 }

-function convertMessageContentToParts(content: MessageContent, isMultimodalModel: boolean): Part[] {
-    if (typeof content === 'string') {
-        return [{ text: content }]
+function convertMessageContentToParts(message: BaseMessage, isMultimodalModel: boolean): Part[] {
+    if (typeof message.content === 'string' && message.content !== '') {
+        return [{ text: message.content }]
    }

-    return content.map((c) => {
-        if (c.type === 'text') {
-            return {
-                text: c.text
-            }
-        }
+    let functionCalls: FunctionCallPart[] = []
+    let functionResponses: FunctionResponsePart[] = []
+    let messageParts: Part[] = []

-        if (c.type === 'tool_use') {
-            return {
-                functionCall: c.functionCall
+    if ('tool_calls' in message && Array.isArray(message.tool_calls) && message.tool_calls.length > 0) {
+        functionCalls = message.tool_calls.map((tc) => ({
+            functionCall: {
+                name: tc.name,
+                args: tc.args
            }
-        }
-
-        /*if (c.type === "tool_use" || c.type === "tool_result") {
-            // TODO: Fix when SDK types are fixed
-            return {
-              ...contentPart,
-              // eslint-disable-next-line @typescript-eslint/no-explicit-any
-            } as any;
-        }*/
-
-        if (c.type === 'image_url') {
-            if (!isMultimodalModel) {
-                throw new Error(`This model does not support images`)
-            }
-            let source
-            if (typeof c.image_url === 'string') {
-                source = c.image_url
-            } else if (typeof c.image_url === 'object' && 'url' in c.image_url) {
-                source = c.image_url.url
-            } else {
-                throw new Error('Please provide image as base64 encoded data URL')
-            }
-            const [dm, data] = source.split(',')
-            if (!dm.startsWith('data:')) {
-                throw new Error('Please provide image as base64 encoded data URL')
-            }
-
-            const [mimeType, encoding] = dm.replace(/^data:/, '').split(';')
-            if (encoding !== 'base64') {
-                throw new Error('Please provide image as base64 encoded data URL')
-            }
-
-            return {
-                inlineData: {
-                    data,
-                    mimeType
+        }))
+    } else if (message._getType() === 'tool' && message.name && message.content) {
+        functionResponses = [
+            {
+                functionResponse: {
+                    name: message.name,
+                    response: message.content
                }
            }
-        }
-        throw new Error(`Unknown content type ${(c as { type: string }).type}`)
-    })
+        ]
+    } else if (Array.isArray(message.content)) {
+        messageParts = message.content.map((c) => {
+            if (c.type === 'text') {
+                return {
+                    text: c.text
+                }
+            }
+
+            if (c.type === 'image_url') {
+                if (!isMultimodalModel) {
+                    throw new Error(`This model does not support images`)
+                }
+                let source
+                if (typeof c.image_url === 'string') {
+                    source = c.image_url
+                } else if (typeof c.image_url === 'object' && 'url' in c.image_url) {
+                    source = c.image_url.url
+                } else {
+                    throw new Error('Please provide image as base64 encoded data URL')
+                }
+                const [dm, data] = source.split(',')
+                if (!dm.startsWith('data:')) {
+                    throw new Error('Please provide image as base64 encoded data URL')
+                }
+
+                const [mimeType, encoding] = dm.replace(/^data:/, '').split(';')
+                if (encoding !== 'base64') {
+                    throw new Error('Please provide image as base64 encoded data URL')
+                }
+
+                return {
+                    inlineData: {
+                        data,
+                        mimeType
+                    }
+                }
+            } else if (c.type === 'media') {
+                return messageContentMedia(c)
+            } else if (c.type === 'tool_use') {
+                return {
+                    functionCall: {
+                        name: c.name,
+                        args: c.input
+                    }
+                }
+            }
+            throw new Error(`Unknown content type ${(c as { type: string }).type}`)
+        })
+    }
+
+    return [...messageParts, ...functionCalls, ...functionResponses]
 }

 /*
@@ -440,7 +529,7 @@ function convertBaseMessagesToContent(messages: BaseMessage[], isMultimodalModel
                throw new Error('Google Generative AI requires alternate messages between authors')
            }

-            const parts = convertMessageContentToParts(message.content, isMultimodalModel)
+            const parts = convertMessageContentToParts(message, isMultimodalModel)

            if (acc.mergeWithPreviousContent) {
                const prevContent = acc.content[acc.content.length - 1]
@@ -454,8 +543,13 @@ function convertBaseMessagesToContent(messages: BaseMessage[], isMultimodalModel
                    content: acc.content
                }
            }
+            let actualRole = role
+            if (actualRole === 'function') {
+                // GenerativeAI API will throw an error if the role is not "user" or "model."
+                actualRole = 'user'
+            }
            const content: Content = {
-                role,
+                role: actualRole,
                parts
            }
            return {
@@ -467,80 +561,80 @@ function convertBaseMessagesToContent(messages: BaseMessage[], isMultimodalModel
    ).content
 }

-function mapGenerateContentResultToChatResult(response: EnhancedGenerateContentResponse): ChatResult {
+function mapGenerateContentResultToChatResult(
+    response: EnhancedGenerateContentResponse,
+    extra?: {
+        usageMetadata: UsageMetadata | undefined
+    }
+): ChatResult {
    // if rejected or error, return empty generations with reason in filters
    if (!response.candidates || response.candidates.length === 0 || !response.candidates[0]) {
        return {
            generations: [],
            llmOutput: {
-                filters: response?.promptFeedback
+                filters: response.promptFeedback
            }
        }
    }

-    const [candidate] = response.candidates
-    const { content, ...generationInfo } = candidate
-    const text = content.parts.map(({ text }) => text).join('')
-
-    if (content.parts.some((part) => part.functionCall)) {
-        const toolCalls: ToolCall[] = []
-        for (const fcPart of content.parts) {
-            const fc = fcPart.functionCall
-            if (fc) {
-                const { name, args } = fc
-                toolCalls.push({ name, args })
-            }
-        }
-
-        const functionCalls = toolCalls.map((tool) => {
-            return { functionCall: { name: tool.name, args: tool.args }, type: 'tool_use' }
-        })
-        const generation: ChatGeneration = {
-            text,
-            message: new AIMessage({
-                content: functionCalls,
-                name: !content ? undefined : content.role,
-                additional_kwargs: generationInfo,
-                tool_calls: toolCalls
-            }),
-            generationInfo
-        }
-        return {
-            generations: [generation]
-        }
-    } else {
-        const generation: ChatGeneration = {
-            text,
-            message: new AIMessage({
-                content: text,
-                name: !content ? undefined : content.role,
-                additional_kwargs: generationInfo
-            }),
-            generationInfo
-        }
-
-        return {
-            generations: [generation]
-        }
-    }
-}
-
-function convertResponseContentToChatGenerationChunk(response: EnhancedGenerateContentResponse): ChatGenerationChunk | null {
-    if (!response.candidates || response.candidates.length === 0) {
-        return null
-    }
+    const functionCalls = response.functionCalls()
    const [candidate] = response.candidates
    const { content, ...generationInfo } = candidate
    const text = content?.parts[0]?.text ?? ''

+    const generation: ChatGeneration = {
+        text,
+        message: new AIMessage({
+            content: text,
+            tool_calls: functionCalls,
+            additional_kwargs: {
+                ...generationInfo
+            },
+            usage_metadata: extra?.usageMetadata as any
+        }),
+        generationInfo
+    }
+
+    return {
+        generations: [generation]
+    }
+}
+
+function convertResponseContentToChatGenerationChunk(
+    response: EnhancedGenerateContentResponse,
+    extra: {
+        usageMetadata?: UsageMetadata | undefined
+        index: number
+    }
+): ChatGenerationChunk | null {
+    if (!response.candidates || response.candidates.length === 0) {
+        return null
+    }
+    const functionCalls = response.functionCalls()
+    const [candidate] = response.candidates
+    const { content, ...generationInfo } = candidate
+    const text = content?.parts[0]?.text ?? ''
+
+    const toolCallChunks: ToolCallChunk[] = []
+    if (functionCalls) {
+        toolCallChunks.push(
+            ...functionCalls.map((fc) => ({
+                ...fc,
+                args: JSON.stringify(fc.args),
+                index: extra.index
+            }))
+        )
+    }
    return new ChatGenerationChunk({
        text,
        message: new AIMessageChunk({
            content: text,
            name: !content ? undefined : content.role,
+            tool_call_chunks: toolCallChunks,
            // Each chunk can have unique "generationInfo", and merging strategy is unclear,
            // so leave blank for now.
-            additional_kwargs: {}
+            additional_kwargs: {},
+            usage_metadata: extra.usageMetadata as any
        }),
        generationInfo
    })
@@ -1,8 +1,9 @@
-import { ChatOllama, ChatOllamaInput } from '@langchain/community/chat_models/ollama'
+import { ChatOllama } from '@langchain/community/chat_models/ollama'
 import { BaseCache } from '@langchain/core/caches'
-import { BaseLLMParams } from '@langchain/core/language_models/llms'
 import { INode, INodeData, INodeParams } from '../../../src/Interface'
 import { getBaseClasses } from '../../../src/utils'
+import { OllamaInput } from '@langchain/community/llms/ollama'
+import { BaseChatModelParams } from '@langchain/core/language_models/chat_models'

 class ChatOllama_ChatModels implements INode {
    label: string
@@ -208,7 +209,7 @@ class ChatOllama_ChatModels implements INode {

        const cache = nodeData.inputs?.cache as BaseCache

-        const obj: ChatOllamaInput & BaseLLMParams = {
+        const obj: OllamaInput & BaseChatModelParams = {
            baseUrl,
            temperature: parseFloat(temperature),
            model: modelName
@@ -439,6 +439,7 @@ class OllamaFunctions extends BaseChatModel<ChatOllamaFunctionsCallOptions> {
        }
    }

+    //@ts-ignore
    override bindTools(
        tools: StructuredToolInterface[],
        kwargs?: Partial<ICommonObject>
@@ -24,8 +24,8 @@ export class ChatOpenAI extends LangchainChatOpenAI implements IVisionChatModal
    }

    revertToOriginalModel(): void {
-        super.modelName = this.configuredModel
-        super.maxTokens = this.configuredMaxToken
+        this.modelName = this.configuredModel
+        this.maxTokens = this.configuredMaxToken
    }

    setMultiModalOption(multiModalOption: IMultiModalOption): void {
@@ -34,8 +34,8 @@ export class ChatOpenAI extends LangchainChatOpenAI implements IVisionChatModal

    setVisionModel(): void {
        if (this.modelName !== 'gpt-4-turbo' && !this.modelName.includes('vision')) {
-            super.modelName = 'gpt-4-turbo'
-            super.maxTokens = this.configuredMaxToken ? this.configuredMaxToken : 1024
+            this.modelName = 'gpt-4-turbo'
+            this.maxTokens = this.configuredMaxToken ? this.configuredMaxToken : 1024
        }
    }
 }
				`@@ -1 +0,0 @@`
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