mirror of
https://github.com/Mintplex-Labs/anything-llm.git
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* refactor stream/chat/embed-stram to be a single execution logic path so that it is easier to maintain and build upon * no thread in sync chat since only api uses it adjust import locations
247 lines
7.1 KiB
JavaScript
247 lines
7.1 KiB
JavaScript
const { chatPrompt } = require("../../chats");
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const { StringOutputParser } = require("langchain/schema/output_parser");
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const { writeResponseChunk } = require("../../helpers/chat/responses");
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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, modelPreference = 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 = modelPreference || 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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this.defaultTemp = 0.7;
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}
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#ollamaClient({ temperature = 0.07 }) {
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const { ChatOllama } = require("langchain/chat_models/ollama");
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return new ChatOllama({
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baseUrl: this.basePath,
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model: this.model,
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temperature,
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});
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}
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// For streaming we use Langchain's wrapper to handle weird chunks
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// or otherwise absorb headaches that can arise from Ollama models
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#convertToLangchainPrototypes(chats = []) {
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const {
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HumanMessage,
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SystemMessage,
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AIMessage,
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} = require("langchain/schema");
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const langchainChats = [];
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const roleToMessageMap = {
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system: SystemMessage,
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user: HumanMessage,
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assistant: AIMessage,
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};
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for (const chat of chats) {
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if (!roleToMessageMap.hasOwnProperty(chat.role)) continue;
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const MessageClass = roleToMessageMap[chat.role];
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langchainChats.push(new MessageClass({ content: chat.content }));
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}
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return langchainChats;
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}
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#appendContext(contextTexts = []) {
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if (!contextTexts || !contextTexts.length) return "";
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return (
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"\nContext:\n" +
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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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}
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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}${this.#appendContext(contextTexts)}`,
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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 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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const model = this.#ollamaClient({
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temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
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});
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const textResponse = await model
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.pipe(new StringOutputParser())
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.invoke(this.#convertToLangchainPrototypes(messages))
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.catch((e) => {
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throw new Error(
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`Ollama::getChatCompletion failed to communicate with Ollama. ${e.message}`
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);
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});
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if (!textResponse || !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 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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const model = this.#ollamaClient({
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temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
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});
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const stream = await model
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.pipe(new StringOutputParser())
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.stream(this.#convertToLangchainPrototypes(messages));
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return stream;
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}
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async getChatCompletion(messages = null, { temperature = 0.7 }) {
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const model = this.#ollamaClient({ temperature });
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const textResponse = await model
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.pipe(new StringOutputParser())
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.invoke(this.#convertToLangchainPrototypes(messages))
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.catch((e) => {
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throw new Error(
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`Ollama::getChatCompletion failed to communicate with Ollama. ${e.message}`
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);
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});
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if (!textResponse || !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 model = this.#ollamaClient({ temperature });
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const stream = await model
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.pipe(new StringOutputParser())
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.stream(this.#convertToLangchainPrototypes(messages));
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return stream;
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}
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handleStream(response, stream, responseProps) {
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const { uuid = uuidv4(), sources = [] } = responseProps;
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return new Promise(async (resolve) => {
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try {
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let fullText = "";
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for await (const chunk of stream) {
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if (chunk === undefined)
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throw new Error(
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"Stream returned undefined chunk. Aborting reply - check model provider logs."
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);
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const content = chunk.hasOwnProperty("content")
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? chunk.content
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: chunk;
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fullText += content;
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writeResponseChunk(response, {
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uuid,
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sources: [],
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type: "textResponseChunk",
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textResponse: content,
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close: false,
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error: false,
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});
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}
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writeResponseChunk(response, {
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uuid,
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sources,
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type: "textResponseChunk",
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textResponse: "",
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close: true,
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error: false,
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});
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resolve(fullText);
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} catch (error) {
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writeResponseChunk(response, {
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uuid,
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sources: [],
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type: "textResponseChunk",
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textResponse: "",
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close: true,
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error: `Ollama:streaming - could not stream chat. ${
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error?.cause ?? error.message
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}`,
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});
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}
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});
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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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