mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-22 09:01:09 +03:00
Feature/Add multi modal to chat ollama (#3499)
* add multi modal to chat ollama * update JSON mode description
This commit is contained in:
Henry Heng
@@ -1,8 +1,9 @@
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import { ChatOllama, ChatOllamaInput } from '@langchain/ollama'
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import { ChatOllamaInput } from '@langchain/ollama'
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import { BaseChatModelParams } from '@langchain/core/language_models/chat_models'
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import { BaseCache } from '@langchain/core/caches'
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import { INode, INodeData, INodeParams } from '../../../src/Interface'
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import { IMultiModalOption, INode, INodeData, INodeParams } from '../../../src/Interface'
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import { getBaseClasses } from '../../../src/utils'
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import { ChatOllama } from './FlowiseChatOllama'
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class ChatOllama_ChatModels implements INode {
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label: string
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@@ -19,7 +20,7 @@ class ChatOllama_ChatModels implements INode {
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constructor() {
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this.label = 'ChatOllama'
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this.name = 'chatOllama'
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this.version = 3.0
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this.version = 4.0
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this.type = 'ChatOllama'
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this.icon = 'Ollama.svg'
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this.category = 'Chat Models'
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@@ -54,6 +55,23 @@ class ChatOllama_ChatModels implements INode {
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default: 0.9,
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optional: true
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},
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{
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label: 'Allow Image Uploads',
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name: 'allowImageUploads',
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type: 'boolean',
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description: 'Allow image uploads for multimodal models. e.g. llama3.2-vision',
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default: false,
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optional: true
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},
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{
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label: 'JSON Mode',
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name: 'jsonMode',
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type: 'boolean',
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description:
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'Coerces model outputs to only return JSON. Specify in the system prompt to return JSON. Ex: Format all responses as JSON object',
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optional: true,
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additionalParams: true
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},
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{
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label: 'Keep Alive',
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name: 'keepAlive',
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@@ -203,6 +221,8 @@ class ChatOllama_ChatModels implements INode {
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const repeatLastN = nodeData.inputs?.repeatLastN as string
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const repeatPenalty = nodeData.inputs?.repeatPenalty as string
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const tfsZ = nodeData.inputs?.tfsZ as string
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const allowImageUploads = nodeData.inputs?.allowImageUploads as boolean
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const jsonMode = nodeData.inputs?.jsonMode as boolean
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const cache = nodeData.inputs?.cache as BaseCache
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@@ -225,8 +245,16 @@ class ChatOllama_ChatModels implements INode {
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if (tfsZ) obj.tfsZ = parseFloat(tfsZ)
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if (keepAlive) obj.keepAlive = keepAlive
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if (cache) obj.cache = cache
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if (jsonMode) obj.format = 'json'
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const model = new ChatOllama(obj)
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const multiModalOption: IMultiModalOption = {
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image: {
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allowImageUploads: allowImageUploads ?? false
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}
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}
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const model = new ChatOllama(nodeData.id, obj)
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model.setMultiModalOption(multiModalOption)
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return model
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}
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}
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@@ -0,0 +1,27 @@
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import { ChatOllama as LCChatOllama, ChatOllamaInput } from '@langchain/ollama'
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import { IMultiModalOption, IVisionChatModal } from '../../../src'
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export class ChatOllama extends LCChatOllama implements IVisionChatModal {
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configuredModel: string
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configuredMaxToken?: number
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multiModalOption: IMultiModalOption
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id: string
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constructor(id: string, fields?: ChatOllamaInput) {
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super(fields)
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this.id = id
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this.configuredModel = fields?.model ?? ''
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}
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revertToOriginalModel(): void {
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this.model = this.configuredModel
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}
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setMultiModalOption(multiModalOption: IMultiModalOption): void {
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this.multiModalOption = multiModalOption
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}
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setVisionModel(): void {
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// pass
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}
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}
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@@ -1,810 +0,0 @@
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import { HumanMessage, AIMessage, BaseMessage, AIMessageChunk, ChatMessage } from '@langchain/core/messages'
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import { ChatResult } from '@langchain/core/outputs'
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import { SimpleChatModel, BaseChatModel, BaseChatModelParams } from '@langchain/core/language_models/chat_models'
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import { SystemMessagePromptTemplate } from '@langchain/core/prompts'
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import { BaseCache } from '@langchain/core/caches'
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import { type StructuredToolInterface } from '@langchain/core/tools'
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import type { BaseFunctionCallOptions, BaseLanguageModelInput } from '@langchain/core/language_models/base'
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import { convertToOpenAIFunction } from '@langchain/core/utils/function_calling'
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import { RunnableInterface } from '@langchain/core/runnables'
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import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
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import { getBaseClasses } from '../../../src/utils'
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import type { BaseLanguageModelCallOptions } from '@langchain/core/language_models/base'
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import { CallbackManagerForLLMRun } from '@langchain/core/callbacks/manager'
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import { ChatGenerationChunk } from '@langchain/core/outputs'
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import type { StringWithAutocomplete } from '@langchain/core/utils/types'
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import { createOllamaChatStream, createOllamaGenerateStream, type OllamaInput, type OllamaMessage } from './utils'
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const DEFAULT_TOOL_SYSTEM_TEMPLATE = `You have access to the following tools:
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{tools}
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You must always select one of the above tools and respond with only a JSON object matching the following schema:
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{{
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"tool": <name of the selected tool>,
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"tool_input": <parameters for the selected tool, matching the tool's JSON schema>
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}}`
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class ChatOllamaFunction_ChatModels implements INode {
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label: string
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name: string
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version: number
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type: string
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icon: string
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category: string
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description: string
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baseClasses: string[]
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credential: INodeParams
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badge?: string
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inputs: INodeParams[]
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constructor() {
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this.label = 'ChatOllama Function'
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this.name = 'chatOllamaFunction'
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this.version = 1.0
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this.type = 'ChatOllamaFunction'
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this.icon = 'Ollama.svg'
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this.category = 'Chat Models'
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this.badge = 'DEPRECATING'
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this.description = 'Run open-source function-calling compatible LLM on Ollama'
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this.baseClasses = [this.type, ...getBaseClasses(OllamaFunctions)]
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this.inputs = [
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{
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label: 'Cache',
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name: 'cache',
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type: 'BaseCache',
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optional: true
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},
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{
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label: 'Base URL',
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name: 'baseUrl',
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type: 'string',
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default: 'http://localhost:11434'
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},
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{
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label: 'Model Name',
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name: 'modelName',
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type: 'string',
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description: 'Only compatible with function calling model like mistral',
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placeholder: 'mistral'
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},
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{
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label: 'Temperature',
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name: 'temperature',
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type: 'number',
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description:
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'The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
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step: 0.1,
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default: 0.9,
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optional: true
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},
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{
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label: 'Tool System Prompt',
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name: 'toolSystemPromptTemplate',
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type: 'string',
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rows: 4,
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description: `Under the hood, Ollama's JSON mode is being used to constrain output to JSON. Output JSON will contains two keys: tool and tool_input fields. We then parse it to execute the tool. Because different models have different strengths, it may be helpful to pass in your own system prompt.`,
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warning: `Prompt must always contains {tools} and instructions to respond with a JSON object with tool and tool_input fields`,
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default: DEFAULT_TOOL_SYSTEM_TEMPLATE,
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placeholder: DEFAULT_TOOL_SYSTEM_TEMPLATE,
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additionalParams: true,
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optional: true
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},
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{
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label: 'Top P',
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name: 'topP',
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type: 'number',
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description:
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'Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
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step: 0.1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Top K',
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name: 'topK',
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type: 'number',
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description:
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'Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
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step: 1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Mirostat',
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name: 'mirostat',
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type: 'number',
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description:
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'Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
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step: 1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Mirostat ETA',
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name: 'mirostatEta',
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type: 'number',
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description:
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'Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1) Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
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step: 0.1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Mirostat TAU',
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name: 'mirostatTau',
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type: 'number',
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description:
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'Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0) Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
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step: 0.1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Context Window Size',
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name: 'numCtx',
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type: 'number',
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description:
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'Sets the size of the context window used to generate the next token. (Default: 2048) Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
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step: 1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Number of GQA groups',
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name: 'numGqa',
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type: 'number',
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description:
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'The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b. Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
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step: 1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Number of GPU',
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name: 'numGpu',
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type: 'number',
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description:
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'The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable. Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
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step: 1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Number of Thread',
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name: 'numThread',
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type: 'number',
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description:
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'Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
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step: 1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Repeat Last N',
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name: 'repeatLastN',
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type: 'number',
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description:
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'Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
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step: 1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Repeat Penalty',
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name: 'repeatPenalty',
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type: 'number',
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description:
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'Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
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step: 0.1,
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optional: true,
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additionalParams: true
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},
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{
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label: 'Stop Sequence',
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name: 'stop',
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type: 'string',
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rows: 4,
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placeholder: 'AI assistant:',
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description:
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'Sets the stop sequences to use. Use comma to seperate different sequences. Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
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optional: true,
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additionalParams: true
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},
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{
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label: 'Tail Free Sampling',
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name: 'tfsZ',
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type: 'number',
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description:
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'Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (Default: 1). Refer to <a target="_blank" href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values">docs</a> for more details',
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step: 0.1,
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optional: true,
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additionalParams: true
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}
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]
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}
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async init(nodeData: INodeData): Promise<any> {
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const temperature = nodeData.inputs?.temperature as string
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const baseUrl = nodeData.inputs?.baseUrl as string
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const modelName = nodeData.inputs?.modelName as string
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const topP = nodeData.inputs?.topP as string
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const topK = nodeData.inputs?.topK as string
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const mirostat = nodeData.inputs?.mirostat as string
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const mirostatEta = nodeData.inputs?.mirostatEta as string
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const mirostatTau = nodeData.inputs?.mirostatTau as string
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const numCtx = nodeData.inputs?.numCtx as string
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const numGqa = nodeData.inputs?.numGqa as string
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const numGpu = nodeData.inputs?.numGpu as string
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const numThread = nodeData.inputs?.numThread as string
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const repeatLastN = nodeData.inputs?.repeatLastN as string
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const repeatPenalty = nodeData.inputs?.repeatPenalty as string
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const stop = nodeData.inputs?.stop as string
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const tfsZ = nodeData.inputs?.tfsZ as string
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const toolSystemPromptTemplate = nodeData.inputs?.toolSystemPromptTemplate as string
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const cache = nodeData.inputs?.cache as BaseCache
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const obj: OllamaFunctionsInput = {
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baseUrl,
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temperature: parseFloat(temperature),
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model: modelName,
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toolSystemPromptTemplate: toolSystemPromptTemplate ? toolSystemPromptTemplate : DEFAULT_TOOL_SYSTEM_TEMPLATE
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}
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if (topP) obj.topP = parseFloat(topP)
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if (topK) obj.topK = parseFloat(topK)
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if (mirostat) obj.mirostat = parseFloat(mirostat)
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if (mirostatEta) obj.mirostatEta = parseFloat(mirostatEta)
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if (mirostatTau) obj.mirostatTau = parseFloat(mirostatTau)
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if (numCtx) obj.numCtx = parseFloat(numCtx)
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if (numGqa) obj.numGqa = parseFloat(numGqa)
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if (numGpu) obj.numGpu = parseFloat(numGpu)
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if (numThread) obj.numThread = parseFloat(numThread)
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if (repeatLastN) obj.repeatLastN = parseFloat(repeatLastN)
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if (repeatPenalty) obj.repeatPenalty = parseFloat(repeatPenalty)
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if (tfsZ) obj.tfsZ = parseFloat(tfsZ)
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if (stop) {
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const stopSequences = stop.split(',')
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obj.stop = stopSequences
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}
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if (cache) obj.cache = cache
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const model = new OllamaFunctions(obj)
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return model
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}
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}
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interface ChatOllamaFunctionsCallOptions extends BaseFunctionCallOptions {}
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type OllamaFunctionsInput = Partial<ChatOllamaInput> &
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BaseChatModelParams & {
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llm?: OllamaChat
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toolSystemPromptTemplate?: string
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}
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class OllamaFunctions extends BaseChatModel<ChatOllamaFunctionsCallOptions> {
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llm: OllamaChat
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fields?: OllamaFunctionsInput
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toolSystemPromptTemplate: string = DEFAULT_TOOL_SYSTEM_TEMPLATE
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protected defaultResponseFunction = {
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name: '__conversational_response',
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description: 'Respond conversationally if no other tools should be called for a given query.',
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parameters: {
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type: 'object',
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properties: {
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response: {
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type: 'string',
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description: 'Conversational response to the user.'
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}
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},
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required: ['response']
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}
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}
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static lc_name(): string {
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return 'OllamaFunctions'
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}
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constructor(fields?: OllamaFunctionsInput) {
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super(fields ?? {})
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this.fields = fields
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this.llm = fields?.llm ?? new OllamaChat({ ...fields, format: 'json' })
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this.toolSystemPromptTemplate = fields?.toolSystemPromptTemplate ?? this.toolSystemPromptTemplate
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}
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invocationParams() {
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return this.llm.invocationParams()
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}
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||||
|
||||
/** @ignore */
|
||||
_identifyingParams() {
|
||||
return this.llm._identifyingParams()
|
||||
}
|
||||
|
||||
async _generate(
|
||||
messages: BaseMessage[],
|
||||
options: this['ParsedCallOptions'],
|
||||
runManager?: CallbackManagerForLLMRun | undefined
|
||||
): Promise<ChatResult> {
|
||||
let functions = options.functions ?? []
|
||||
if (options.function_call !== undefined) {
|
||||
functions = functions.filter((fn) => fn.name === options.function_call?.name)
|
||||
if (!functions.length) {
|
||||
throw new Error(`If "function_call" is specified, you must also pass a matching function in "functions".`)
|
||||
}
|
||||
} else if (functions.length === 0) {
|
||||
functions.push(this.defaultResponseFunction)
|
||||
}
|
||||
const systemPromptTemplate = SystemMessagePromptTemplate.fromTemplate(this.toolSystemPromptTemplate)
|
||||
const systemMessage = await systemPromptTemplate.format({
|
||||
tools: JSON.stringify(functions, null, 2)
|
||||
})
|
||||
|
||||
let generatedMessages = [systemMessage, ...messages]
|
||||
let isToolResponse = false
|
||||
if (
|
||||
messages.length > 3 &&
|
||||
messages[messages.length - 1]._getType() === 'tool' &&
|
||||
functions.length &&
|
||||
messages[messages.length - 1].additional_kwargs?.name === functions[0].name
|
||||
) {
|
||||
const lastToolQuestion = messages[messages.length - 3].content
|
||||
const lastToolResp = messages.pop()?.content
|
||||
// Pop the message again to get rid of tool call message
|
||||
messages.pop()?.content
|
||||
const humanMessage = new HumanMessage({
|
||||
content: `Given user question: ${lastToolQuestion} and answer: ${lastToolResp}\n\nWrite a natural language response`
|
||||
})
|
||||
generatedMessages = [...messages, humanMessage]
|
||||
isToolResponse = true
|
||||
this.llm = new OllamaChat({ ...this.fields })
|
||||
}
|
||||
const chatResult = await this.llm._generate(generatedMessages, options, runManager)
|
||||
const chatGenerationContent = chatResult.generations[0].message.content
|
||||
|
||||
if (typeof chatGenerationContent !== 'string') {
|
||||
throw new Error('OllamaFunctions does not support non-string output.')
|
||||
}
|
||||
|
||||
if (isToolResponse) {
|
||||
return {
|
||||
generations: [
|
||||
{
|
||||
message: new AIMessage({
|
||||
content: chatGenerationContent
|
||||
}),
|
||||
text: chatGenerationContent
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
let parsedChatResult
|
||||
try {
|
||||
parsedChatResult = JSON.parse(chatGenerationContent)
|
||||
} catch (e) {
|
||||
throw new Error(`"${this.llm.model}" did not respond with valid JSON. Please try again.`)
|
||||
}
|
||||
|
||||
const calledToolName = parsedChatResult.tool
|
||||
const calledToolArguments = parsedChatResult.tool_input
|
||||
const calledTool = functions.find((fn) => fn.name === calledToolName)
|
||||
if (calledTool === undefined) {
|
||||
throw new Error(`Failed to parse a function call from ${this.llm.model} output: ${chatGenerationContent}`)
|
||||
}
|
||||
|
||||
if (calledTool.name === this.defaultResponseFunction.name) {
|
||||
return {
|
||||
generations: [
|
||||
{
|
||||
message: new AIMessage({
|
||||
content: calledToolArguments.response
|
||||
}),
|
||||
text: calledToolArguments.response
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
const responseMessageWithFunctions = new AIMessage({
|
||||
content: '',
|
||||
tool_calls: [
|
||||
{
|
||||
name: calledToolName,
|
||||
args: calledToolArguments || {}
|
||||
}
|
||||
],
|
||||
invalid_tool_calls: [],
|
||||
additional_kwargs: {
|
||||
function_call: {
|
||||
name: calledToolName,
|
||||
arguments: calledToolArguments ? JSON.stringify(calledToolArguments) : ''
|
||||
},
|
||||
tool_calls: [
|
||||
{
|
||||
id: Date.now().toString(),
|
||||
type: 'function',
|
||||
function: {
|
||||
name: calledToolName,
|
||||
arguments: calledToolArguments ? JSON.stringify(calledToolArguments) : ''
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
})
|
||||
|
||||
return {
|
||||
generations: [{ message: responseMessageWithFunctions, text: '' }]
|
||||
}
|
||||
}
|
||||
|
||||
//@ts-ignore
|
||||
override bindTools(
|
||||
tools: StructuredToolInterface[],
|
||||
kwargs?: Partial<ICommonObject>
|
||||
): RunnableInterface<BaseLanguageModelInput, AIMessageChunk, ICommonObject> {
|
||||
return this.bind({
|
||||
functions: tools.map((tool) => convertToOpenAIFunction(tool)),
|
||||
...kwargs
|
||||
} as Partial<ICommonObject>)
|
||||
}
|
||||
|
||||
_llmType(): string {
|
||||
return 'ollama_functions'
|
||||
}
|
||||
|
||||
/** @ignore */
|
||||
_combineLLMOutput() {
|
||||
return []
|
||||
}
|
||||
}
|
||||
|
||||
export interface ChatOllamaInput extends OllamaInput {}
|
||||
|
||||
interface ChatOllamaCallOptions extends BaseLanguageModelCallOptions {}
|
||||
|
||||
class OllamaChat extends SimpleChatModel<ChatOllamaCallOptions> implements ChatOllamaInput {
|
||||
static lc_name() {
|
||||
return 'ChatOllama'
|
||||
}
|
||||
|
||||
lc_serializable = true
|
||||
|
||||
model = 'llama2'
|
||||
|
||||
baseUrl = 'http://localhost:11434'
|
||||
|
||||
keepAlive = '5m'
|
||||
|
||||
embeddingOnly?: boolean
|
||||
|
||||
f16KV?: boolean
|
||||
|
||||
frequencyPenalty?: number
|
||||
|
||||
headers?: Record<string, string>
|
||||
|
||||
logitsAll?: boolean
|
||||
|
||||
lowVram?: boolean
|
||||
|
||||
mainGpu?: number
|
||||
|
||||
mirostat?: number
|
||||
|
||||
mirostatEta?: number
|
||||
|
||||
mirostatTau?: number
|
||||
|
||||
numBatch?: number
|
||||
|
||||
numCtx?: number
|
||||
|
||||
numGpu?: number
|
||||
|
||||
numGqa?: number
|
||||
|
||||
numKeep?: number
|
||||
|
||||
numPredict?: number
|
||||
|
||||
numThread?: number
|
||||
|
||||
penalizeNewline?: boolean
|
||||
|
||||
presencePenalty?: number
|
||||
|
||||
repeatLastN?: number
|
||||
|
||||
repeatPenalty?: number
|
||||
|
||||
ropeFrequencyBase?: number
|
||||
|
||||
ropeFrequencyScale?: number
|
||||
|
||||
temperature?: number
|
||||
|
||||
stop?: string[]
|
||||
|
||||
tfsZ?: number
|
||||
|
||||
topK?: number
|
||||
|
||||
topP?: number
|
||||
|
||||
typicalP?: number
|
||||
|
||||
useMLock?: boolean
|
||||
|
||||
useMMap?: boolean
|
||||
|
||||
vocabOnly?: boolean
|
||||
|
||||
format?: StringWithAutocomplete<'json'>
|
||||
|
||||
constructor(fields: OllamaInput & BaseChatModelParams) {
|
||||
super(fields)
|
||||
this.model = fields.model ?? this.model
|
||||
this.baseUrl = fields.baseUrl?.endsWith('/') ? fields.baseUrl.slice(0, -1) : fields.baseUrl ?? this.baseUrl
|
||||
this.keepAlive = fields.keepAlive ?? this.keepAlive
|
||||
this.embeddingOnly = fields.embeddingOnly
|
||||
this.f16KV = fields.f16KV
|
||||
this.frequencyPenalty = fields.frequencyPenalty
|
||||
this.headers = fields.headers
|
||||
this.logitsAll = fields.logitsAll
|
||||
this.lowVram = fields.lowVram
|
||||
this.mainGpu = fields.mainGpu
|
||||
this.mirostat = fields.mirostat
|
||||
this.mirostatEta = fields.mirostatEta
|
||||
this.mirostatTau = fields.mirostatTau
|
||||
this.numBatch = fields.numBatch
|
||||
this.numCtx = fields.numCtx
|
||||
this.numGpu = fields.numGpu
|
||||
this.numGqa = fields.numGqa
|
||||
this.numKeep = fields.numKeep
|
||||
this.numPredict = fields.numPredict
|
||||
this.numThread = fields.numThread
|
||||
this.penalizeNewline = fields.penalizeNewline
|
||||
this.presencePenalty = fields.presencePenalty
|
||||
this.repeatLastN = fields.repeatLastN
|
||||
this.repeatPenalty = fields.repeatPenalty
|
||||
this.ropeFrequencyBase = fields.ropeFrequencyBase
|
||||
this.ropeFrequencyScale = fields.ropeFrequencyScale
|
||||
this.temperature = fields.temperature
|
||||
this.stop = fields.stop
|
||||
this.tfsZ = fields.tfsZ
|
||||
this.topK = fields.topK
|
||||
this.topP = fields.topP
|
||||
this.typicalP = fields.typicalP
|
||||
this.useMLock = fields.useMLock
|
||||
this.useMMap = fields.useMMap
|
||||
this.vocabOnly = fields.vocabOnly
|
||||
this.format = fields.format
|
||||
}
|
||||
|
||||
_llmType() {
|
||||
return 'ollama'
|
||||
}
|
||||
|
||||
/**
|
||||
* A method that returns the parameters for an Ollama API call. It
|
||||
* includes model and options parameters.
|
||||
* @param options Optional parsed call options.
|
||||
* @returns An object containing the parameters for an Ollama API call.
|
||||
*/
|
||||
invocationParams(options?: this['ParsedCallOptions']) {
|
||||
return {
|
||||
model: this.model,
|
||||
format: this.format,
|
||||
keep_alive: this.keepAlive,
|
||||
options: {
|
||||
embedding_only: this.embeddingOnly,
|
||||
f16_kv: this.f16KV,
|
||||
frequency_penalty: this.frequencyPenalty,
|
||||
logits_all: this.logitsAll,
|
||||
low_vram: this.lowVram,
|
||||
main_gpu: this.mainGpu,
|
||||
mirostat: this.mirostat,
|
||||
mirostat_eta: this.mirostatEta,
|
||||
mirostat_tau: this.mirostatTau,
|
||||
num_batch: this.numBatch,
|
||||
num_ctx: this.numCtx,
|
||||
num_gpu: this.numGpu,
|
||||
num_gqa: this.numGqa,
|
||||
num_keep: this.numKeep,
|
||||
num_predict: this.numPredict,
|
||||
num_thread: this.numThread,
|
||||
penalize_newline: this.penalizeNewline,
|
||||
presence_penalty: this.presencePenalty,
|
||||
repeat_last_n: this.repeatLastN,
|
||||
repeat_penalty: this.repeatPenalty,
|
||||
rope_frequency_base: this.ropeFrequencyBase,
|
||||
rope_frequency_scale: this.ropeFrequencyScale,
|
||||
temperature: this.temperature,
|
||||
stop: options?.stop ?? this.stop,
|
||||
tfs_z: this.tfsZ,
|
||||
top_k: this.topK,
|
||||
top_p: this.topP,
|
||||
typical_p: this.typicalP,
|
||||
use_mlock: this.useMLock,
|
||||
use_mmap: this.useMMap,
|
||||
vocab_only: this.vocabOnly
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
_combineLLMOutput() {
|
||||
return {}
|
||||
}
|
||||
|
||||
/** @deprecated */
|
||||
async *_streamResponseChunksLegacy(
|
||||
input: BaseMessage[],
|
||||
options: this['ParsedCallOptions'],
|
||||
runManager?: CallbackManagerForLLMRun
|
||||
): AsyncGenerator<ChatGenerationChunk> {
|
||||
const stream = createOllamaGenerateStream(
|
||||
this.baseUrl,
|
||||
{
|
||||
...this.invocationParams(options),
|
||||
prompt: this._formatMessagesAsPrompt(input)
|
||||
},
|
||||
{
|
||||
...options,
|
||||
headers: this.headers
|
||||
}
|
||||
)
|
||||
for await (const chunk of stream) {
|
||||
if (!chunk.done) {
|
||||
yield new ChatGenerationChunk({
|
||||
text: chunk.response,
|
||||
message: new AIMessageChunk({ content: chunk.response })
|
||||
})
|
||||
await runManager?.handleLLMNewToken(chunk.response ?? '')
|
||||
} else {
|
||||
yield new ChatGenerationChunk({
|
||||
text: '',
|
||||
message: new AIMessageChunk({ content: '' }),
|
||||
generationInfo: {
|
||||
model: chunk.model,
|
||||
total_duration: chunk.total_duration,
|
||||
load_duration: chunk.load_duration,
|
||||
prompt_eval_count: chunk.prompt_eval_count,
|
||||
prompt_eval_duration: chunk.prompt_eval_duration,
|
||||
eval_count: chunk.eval_count,
|
||||
eval_duration: chunk.eval_duration
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async *_streamResponseChunks(
|
||||
input: BaseMessage[],
|
||||
options: this['ParsedCallOptions'],
|
||||
runManager?: CallbackManagerForLLMRun
|
||||
): AsyncGenerator<ChatGenerationChunk> {
|
||||
try {
|
||||
const stream = await this.caller.call(async () =>
|
||||
createOllamaChatStream(
|
||||
this.baseUrl,
|
||||
{
|
||||
...this.invocationParams(options),
|
||||
messages: this._convertMessagesToOllamaMessages(input)
|
||||
},
|
||||
{
|
||||
...options,
|
||||
headers: this.headers
|
||||
}
|
||||
)
|
||||
)
|
||||
for await (const chunk of stream) {
|
||||
if (!chunk.done) {
|
||||
yield new ChatGenerationChunk({
|
||||
text: chunk.message.content,
|
||||
message: new AIMessageChunk({ content: chunk.message.content })
|
||||
})
|
||||
await runManager?.handleLLMNewToken(chunk.message.content ?? '')
|
||||
} else {
|
||||
yield new ChatGenerationChunk({
|
||||
text: '',
|
||||
message: new AIMessageChunk({ content: '' }),
|
||||
generationInfo: {
|
||||
model: chunk.model,
|
||||
total_duration: chunk.total_duration,
|
||||
load_duration: chunk.load_duration,
|
||||
prompt_eval_count: chunk.prompt_eval_count,
|
||||
prompt_eval_duration: chunk.prompt_eval_duration,
|
||||
eval_count: chunk.eval_count,
|
||||
eval_duration: chunk.eval_duration
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
} catch (e: any) {
|
||||
if (e.response?.status === 404) {
|
||||
console.warn(
|
||||
'[WARNING]: It seems you are using a legacy version of Ollama. Please upgrade to a newer version for better chat support.'
|
||||
)
|
||||
yield* this._streamResponseChunksLegacy(input, options, runManager)
|
||||
} else {
|
||||
throw e
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
protected _convertMessagesToOllamaMessages(messages: BaseMessage[]): OllamaMessage[] {
|
||||
return messages.map((message) => {
|
||||
let role
|
||||
if (message._getType() === 'human') {
|
||||
role = 'user'
|
||||
} else if (message._getType() === 'ai' || message._getType() === 'tool') {
|
||||
role = 'assistant'
|
||||
} else if (message._getType() === 'system') {
|
||||
role = 'system'
|
||||
} else {
|
||||
throw new Error(`Unsupported message type for Ollama: ${message._getType()}`)
|
||||
}
|
||||
let content = ''
|
||||
const images = []
|
||||
if (typeof message.content === 'string') {
|
||||
content = message.content
|
||||
} else {
|
||||
for (const contentPart of message.content) {
|
||||
if (contentPart.type === 'text') {
|
||||
content = `${content}\n${contentPart.text}`
|
||||
} else if (contentPart.type === 'image_url' && typeof contentPart.image_url === 'string') {
|
||||
const imageUrlComponents = contentPart.image_url.split(',')
|
||||
// Support both data:image/jpeg;base64,<image> format as well
|
||||
images.push(imageUrlComponents[1] ?? imageUrlComponents[0])
|
||||
} else {
|
||||
throw new Error(
|
||||
`Unsupported message content type. Must either have type "text" or type "image_url" with a string "image_url" field.`
|
||||
)
|
||||
}
|
||||
}
|
||||
}
|
||||
return {
|
||||
role,
|
||||
content,
|
||||
images
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
/** @deprecated */
|
||||
protected _formatMessagesAsPrompt(messages: BaseMessage[]): string {
|
||||
const formattedMessages = messages
|
||||
.map((message) => {
|
||||
let messageText
|
||||
if (message._getType() === 'human') {
|
||||
messageText = `[INST] ${message.content} [/INST]`
|
||||
} else if (message._getType() === 'ai') {
|
||||
messageText = message.content
|
||||
} else if (message._getType() === 'system') {
|
||||
messageText = `<<SYS>> ${message.content} <</SYS>>`
|
||||
} else if (ChatMessage.isInstance(message)) {
|
||||
messageText = `\n\n${message.role[0].toUpperCase()}${message.role.slice(1)}: ${message.content}`
|
||||
} else {
|
||||
console.warn(`Unsupported message type passed to Ollama: "${message._getType()}"`)
|
||||
messageText = ''
|
||||
}
|
||||
return messageText
|
||||
})
|
||||
.join('\n')
|
||||
return formattedMessages
|
||||
}
|
||||
|
||||
/** @ignore */
|
||||
async _call(messages: BaseMessage[], options: this['ParsedCallOptions'], runManager?: CallbackManagerForLLMRun): Promise<string> {
|
||||
const chunks = []
|
||||
for await (const chunk of this._streamResponseChunks(messages, options, runManager)) {
|
||||
chunks.push(chunk.message.content)
|
||||
}
|
||||
return chunks.join('')
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { nodeClass: ChatOllamaFunction_ChatModels }
|
||||
@@ -1 +0,0 @@
|
||||
<svg width="32" height="32" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M7 27.5c0-1.273.388-2.388.97-3-.582-.612-.97-1.727-.97-3 0-1.293.4-2.422.996-3.028A4.818 4.818 0 0 1 7 15.5c0-2.485 1.79-4.5 4-4.5l.1.001a5.002 5.002 0 0 1 9.8 0L21 11c2.21 0 4 2.015 4 4.5 0 1.139-.376 2.18-.996 2.972.595.606.996 1.735.996 3.028 0 1.273-.389 2.388-.97 3 .581.612.97 1.727.97 3" stroke="#000" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/><path d="M9.5 11C9.167 8.5 9 4 11 4c1.5 0 1.667 2.667 2 4m9.5 3c.333-2.5.5-7-1.5-7-1.5 0-1.667 2.667-2 4" stroke="#000" stroke-width="2" stroke-linecap="round"/><circle cx="11" cy="15" r="1" fill="#000"/><circle cx="21" cy="15" r="1" fill="#000"/><path d="M13 17c0-2 2-2.5 3-2.5s3 .5 3 2.5-2 2.5-3 2.5-3-.5-3-2.5Z" stroke="#000" stroke-width="2" stroke-linecap="round"/></svg>
|
||||
|
Before Width: | Height: | Size: 834 B |
@@ -1,185 +0,0 @@
|
||||
import { IterableReadableStream } from '@langchain/core/utils/stream'
|
||||
import type { StringWithAutocomplete } from '@langchain/core/utils/types'
|
||||
import { BaseLanguageModelCallOptions } from '@langchain/core/language_models/base'
|
||||
|
||||
export interface OllamaInput {
|
||||
embeddingOnly?: boolean
|
||||
f16KV?: boolean
|
||||
frequencyPenalty?: number
|
||||
headers?: Record<string, string>
|
||||
keepAlive?: string
|
||||
logitsAll?: boolean
|
||||
lowVram?: boolean
|
||||
mainGpu?: number
|
||||
model?: string
|
||||
baseUrl?: string
|
||||
mirostat?: number
|
||||
mirostatEta?: number
|
||||
mirostatTau?: number
|
||||
numBatch?: number
|
||||
numCtx?: number
|
||||
numGpu?: number
|
||||
numGqa?: number
|
||||
numKeep?: number
|
||||
numPredict?: number
|
||||
numThread?: number
|
||||
penalizeNewline?: boolean
|
||||
presencePenalty?: number
|
||||
repeatLastN?: number
|
||||
repeatPenalty?: number
|
||||
ropeFrequencyBase?: number
|
||||
ropeFrequencyScale?: number
|
||||
temperature?: number
|
||||
stop?: string[]
|
||||
tfsZ?: number
|
||||
topK?: number
|
||||
topP?: number
|
||||
typicalP?: number
|
||||
useMLock?: boolean
|
||||
useMMap?: boolean
|
||||
vocabOnly?: boolean
|
||||
format?: StringWithAutocomplete<'json'>
|
||||
}
|
||||
|
||||
export interface OllamaRequestParams {
|
||||
model: string
|
||||
format?: StringWithAutocomplete<'json'>
|
||||
images?: string[]
|
||||
options: {
|
||||
embedding_only?: boolean
|
||||
f16_kv?: boolean
|
||||
frequency_penalty?: number
|
||||
logits_all?: boolean
|
||||
low_vram?: boolean
|
||||
main_gpu?: number
|
||||
mirostat?: number
|
||||
mirostat_eta?: number
|
||||
mirostat_tau?: number
|
||||
num_batch?: number
|
||||
num_ctx?: number
|
||||
num_gpu?: number
|
||||
num_gqa?: number
|
||||
num_keep?: number
|
||||
num_thread?: number
|
||||
num_predict?: number
|
||||
penalize_newline?: boolean
|
||||
presence_penalty?: number
|
||||
repeat_last_n?: number
|
||||
repeat_penalty?: number
|
||||
rope_frequency_base?: number
|
||||
rope_frequency_scale?: number
|
||||
temperature?: number
|
||||
stop?: string[]
|
||||
tfs_z?: number
|
||||
top_k?: number
|
||||
top_p?: number
|
||||
typical_p?: number
|
||||
use_mlock?: boolean
|
||||
use_mmap?: boolean
|
||||
vocab_only?: boolean
|
||||
}
|
||||
}
|
||||
|
||||
export type OllamaMessage = {
|
||||
role: StringWithAutocomplete<'user' | 'assistant' | 'system'>
|
||||
content: string
|
||||
images?: string[]
|
||||
}
|
||||
|
||||
export interface OllamaGenerateRequestParams extends OllamaRequestParams {
|
||||
prompt: string
|
||||
}
|
||||
|
||||
export interface OllamaChatRequestParams extends OllamaRequestParams {
|
||||
messages: OllamaMessage[]
|
||||
}
|
||||
|
||||
export type BaseOllamaGenerationChunk = {
|
||||
model: string
|
||||
created_at: string
|
||||
done: boolean
|
||||
total_duration?: number
|
||||
load_duration?: number
|
||||
prompt_eval_count?: number
|
||||
prompt_eval_duration?: number
|
||||
eval_count?: number
|
||||
eval_duration?: number
|
||||
}
|
||||
|
||||
export type OllamaGenerationChunk = BaseOllamaGenerationChunk & {
|
||||
response: string
|
||||
}
|
||||
|
||||
export type OllamaChatGenerationChunk = BaseOllamaGenerationChunk & {
|
||||
message: OllamaMessage
|
||||
}
|
||||
|
||||
export type OllamaCallOptions = BaseLanguageModelCallOptions & {
|
||||
headers?: Record<string, string>
|
||||
}
|
||||
|
||||
async function* createOllamaStream(url: string, params: OllamaRequestParams, options: OllamaCallOptions) {
|
||||
let formattedUrl = url
|
||||
if (formattedUrl.startsWith('http://localhost:')) {
|
||||
// Node 18 has issues with resolving "localhost"
|
||||
// See https://github.com/node-fetch/node-fetch/issues/1624
|
||||
formattedUrl = formattedUrl.replace('http://localhost:', 'http://127.0.0.1:')
|
||||
}
|
||||
const response = await fetch(formattedUrl, {
|
||||
method: 'POST',
|
||||
body: JSON.stringify(params),
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
...options.headers
|
||||
},
|
||||
signal: options.signal
|
||||
})
|
||||
if (!response.ok) {
|
||||
let error
|
||||
const responseText = await response.text()
|
||||
try {
|
||||
const json = JSON.parse(responseText)
|
||||
error = new Error(`Ollama call failed with status code ${response.status}: ${json.error}`)
|
||||
} catch (e) {
|
||||
error = new Error(`Ollama call failed with status code ${response.status}: ${responseText}`)
|
||||
}
|
||||
;(error as any).response = response
|
||||
throw error
|
||||
}
|
||||
if (!response.body) {
|
||||
throw new Error('Could not begin Ollama stream. Please check the given URL and try again.')
|
||||
}
|
||||
|
||||
const stream = IterableReadableStream.fromReadableStream(response.body)
|
||||
|
||||
const decoder = new TextDecoder()
|
||||
let extra = ''
|
||||
for await (const chunk of stream) {
|
||||
const decoded = extra + decoder.decode(chunk)
|
||||
const lines = decoded.split('\n')
|
||||
extra = lines.pop() || ''
|
||||
for (const line of lines) {
|
||||
try {
|
||||
yield JSON.parse(line)
|
||||
} catch (e) {
|
||||
console.warn(`Received a non-JSON parseable chunk: ${line}`)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
export async function* createOllamaGenerateStream(
|
||||
baseUrl: string,
|
||||
params: OllamaGenerateRequestParams,
|
||||
options: OllamaCallOptions
|
||||
): AsyncGenerator<OllamaGenerationChunk> {
|
||||
yield* createOllamaStream(`${baseUrl}/api/generate`, params, options)
|
||||
}
|
||||
|
||||
export async function* createOllamaChatStream(
|
||||
baseUrl: string,
|
||||
params: OllamaChatRequestParams,
|
||||
options: OllamaCallOptions
|
||||
): AsyncGenerator<OllamaChatGenerationChunk> {
|
||||
yield* createOllamaStream(`${baseUrl}/api/chat`, params, options)
|
||||
}
|
||||
@@ -92,7 +92,7 @@ export const utilGetUploadsConfig = async (chatflowid: string): Promise<IUploadC
|
||||
'supervisor',
|
||||
'seqStart'
|
||||
]
|
||||
const imgUploadLLMNodes = ['chatOpenAI', 'chatAnthropic', 'awsChatBedrock', 'azureChatOpenAI', 'chatGoogleGenerativeAI']
|
||||
const imgUploadLLMNodes = ['chatOpenAI', 'chatAnthropic', 'awsChatBedrock', 'azureChatOpenAI', 'chatGoogleGenerativeAI', 'chatOllama']
|
||||
|
||||
if (nodes.some((node) => imgUploadAllowedNodes.includes(node.data.name))) {
|
||||
nodes.forEach((node: IReactFlowNode) => {
|
||||
|
||||
Reference in New Issue
Block a user