mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-09-11 23:03:25 +02:00
209 lines
5.7 KiB
JavaScript
209 lines
5.7 KiB
JavaScript
const { chatPrompt } = require("../../chats");
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// Docs: https://github.com/jmorganca/ollama/blob/main/docs/api.md
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class OllamaAILLM {
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constructor(embedder = null) {
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if (!process.env.OLLAMA_BASE_PATH)
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throw new Error("No Ollama Base Path was set.");
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this.basePath = process.env.OLLAMA_BASE_PATH;
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this.model = process.env.OLLAMA_MODEL_PREF;
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this.limits = {
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history: this.promptWindowLimit() * 0.15,
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system: this.promptWindowLimit() * 0.15,
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user: this.promptWindowLimit() * 0.7,
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};
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if (!embedder)
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throw new Error(
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"INVALID OLLAMA SETUP. No embedding engine has been set. Go to instance settings and set up an embedding interface to use Ollama as your LLM."
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);
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this.embedder = embedder;
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}
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streamingEnabled() {
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return "streamChat" in this && "streamGetChatCompletion" in this;
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}
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// Ensure the user set a value for the token limit
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// and if undefined - assume 4096 window.
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promptWindowLimit() {
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const limit = process.env.OLLAMA_MODEL_TOKEN_LIMIT || 4096;
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if (!limit || isNaN(Number(limit)))
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throw new Error("No Ollama token context limit was set.");
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return Number(limit);
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}
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async isValidChatCompletionModel(_ = "") {
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return true;
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}
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constructPrompt({
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systemPrompt = "",
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contextTexts = [],
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chatHistory = [],
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userPrompt = "",
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}) {
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const prompt = {
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role: "system",
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content: `${systemPrompt}
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Context:
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${contextTexts
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.map((text, i) => {
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return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
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})
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.join("")}`,
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};
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return [prompt, ...chatHistory, { role: "user", content: userPrompt }];
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}
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async isSafe(_input = "") {
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// Not implemented so must be stubbed
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return { safe: true, reasons: [] };
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}
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async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
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const textResponse = await fetch(`${this.basePath}/api/chat`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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body: JSON.stringify({
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model: this.model,
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stream: false,
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options: {
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temperature: Number(workspace?.openAiTemp ?? 0.7),
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},
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messages: await this.compressMessages(
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{
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systemPrompt: chatPrompt(workspace),
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userPrompt: prompt,
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chatHistory,
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},
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rawHistory
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),
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}),
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})
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.then((res) => {
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if (!res.ok)
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throw new Error(`Ollama:sendChat ${res.status} ${res.statusText}`);
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return res.json();
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})
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.then((data) => data?.message?.content)
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.catch((e) => {
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console.error(e);
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throw new Error(`Ollama::sendChat failed with: ${error.message}`);
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});
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if (!textResponse.length)
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throw new Error(`Ollama::sendChat text response was empty.`);
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return textResponse;
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}
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async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
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const response = await fetch(`${this.basePath}/api/chat`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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body: JSON.stringify({
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model: this.model,
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stream: true,
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options: {
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temperature: Number(workspace?.openAiTemp ?? 0.7),
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},
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messages: await this.compressMessages(
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{
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systemPrompt: chatPrompt(workspace),
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userPrompt: prompt,
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chatHistory,
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},
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rawHistory
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),
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}),
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}).catch((e) => {
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console.error(e);
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throw new Error(`Ollama:streamChat ${error.message}`);
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});
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return { type: "ollamaStream", response };
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}
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async getChatCompletion(messages = null, { temperature = 0.7 }) {
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const textResponse = await fetch(`${this.basePath}/api/chat`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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body: JSON.stringify({
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model: this.model,
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messages,
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stream: false,
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options: {
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temperature,
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},
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}),
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})
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.then((res) => {
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if (!res.ok)
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throw new Error(
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`Ollama:getChatCompletion ${res.status} ${res.statusText}`
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);
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return res.json();
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})
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.then((data) => data?.message?.content)
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.catch((e) => {
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console.error(e);
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throw new Error(
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`Ollama::getChatCompletion failed with: ${error.message}`
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);
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});
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if (!textResponse.length)
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throw new Error(`Ollama::getChatCompletion text response was empty.`);
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return textResponse;
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}
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async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
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const response = await fetch(`${this.basePath}/api/chat`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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body: JSON.stringify({
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model: this.model,
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stream: true,
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messages,
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options: {
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temperature,
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},
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}),
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}).catch((e) => {
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console.error(e);
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throw new Error(`Ollama:streamGetChatCompletion ${error.message}`);
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});
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return { type: "ollamaStream", response };
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}
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// Simple wrapper for dynamic embedder & normalize interface for all LLM implementations
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async embedTextInput(textInput) {
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return await this.embedder.embedTextInput(textInput);
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}
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async embedChunks(textChunks = []) {
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return await this.embedder.embedChunks(textChunks);
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}
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async compressMessages(promptArgs = {}, rawHistory = []) {
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const { messageArrayCompressor } = require("../../helpers/chat");
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const messageArray = this.constructPrompt(promptArgs);
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return await messageArrayCompressor(this, messageArray, rawHistory);
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}
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}
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module.exports = {
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OllamaAILLM,
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};
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