mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-11-14 02:20:12 +01:00
449 lines
18 KiB
JavaScript
449 lines
18 KiB
JavaScript
const { sourceIdentifier } = require("../../chats");
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const { safeJsonParse } = require("../../http");
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const { TokenManager } = require("../tiktoken");
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const { convertToPromptHistory } = require("./responses");
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/*
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What is the message Array compressor?
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TLDR: So anyway, i started blasting (your prompts & stuff)
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messageArrayCompressor arose out of a need for users to be able to insert unlimited token prompts
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and also maintain coherent history, system instructions and context, if applicable.
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We took an opinionated approach that after much back-testing we have found retained a highly coherent answer
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under most user conditions that a user would take while using this specific system. While other systems may
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use a more advanced model for compressing message history or simplify text through a recursive approach - our is much more simple.
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We "cannonball" the input.
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Cannonball (verb): To ensure a prompt fits through a model window we blast a hole in the center of any inputs blocking our path to doing so.
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This starts by dissecting the input as tokens and delete from the middle-out bi-directionally until the prompt window is satisfied.
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You may think: "Doesn't this result in massive data loss?" - yes & no.
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Under the use cases we expect the tool to be used, which is mostly chatting with documents, we are able to use this approach with minimal blowback
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on the quality of responses.
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We accomplish this by taking a rate-limit approach that is proportional to the model capacity. Since we support more than openAI models, this needs to
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be generic and reliance on a "better summary" model just is not a luxury we can afford. The added latency overhead during prompting is also unacceptable.
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In general:
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system: at best 15% of token capacity
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history: at best 15% of token capacity
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prompt: at best 70% of token capacity.
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we handle overflows by taking an aggressive path for two main cases.
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1. Very large user prompt
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- Likely uninterested in context, history, or even system prompt. This is a "standalone" prompt that highjacks the whole thread.
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- We run this prompt on its own since a prompt that is over 70% of context window certainly is standalone.
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2. Context window is exceeded in regular use.
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- We do not touch prompt since it is very likely to be <70% of window.
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- We check system prompt is not outrageous - if it is we cannonball it and keep context if present.
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- We check a sliding window of history, only allowing up to 15% of the history to pass through if it fits, with a
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preference for recent history if we can cannonball to fit it, otherwise it is omitted.
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We end up with a rather large prompt that fits through a given window with a lot of room for response in most use-cases.
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We also take the approach that history is the least important and most flexible of the items in this array of responses.
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There is a supplemental version of this function that also returns a formatted string for models like Claude-2
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*/
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async function messageArrayCompressor(llm, messages = [], rawHistory = []) {
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// assume the response will be at least 600 tokens. If the total prompt + reply is over we need to proactively
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// run the compressor to ensure the prompt has enough space to reply.
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// realistically - most users will not be impacted by this.
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const tokenBuffer = 600;
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const tokenManager = new TokenManager(llm.model);
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// If no work needs to be done, just pass through.
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if (tokenManager.statsFrom(messages) + tokenBuffer < llm.promptWindowLimit())
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return messages;
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const system = messages.shift();
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const user = messages.pop();
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const userPromptSize = tokenManager.countFromString(user.content);
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// User prompt is the main focus here - we we prioritize it and allow
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// it to highjack the entire conversation thread. We are going to
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// cannonball the prompt through to ensure the reply has at least 20% of
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// the token supply to reply with.
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if (userPromptSize > llm.limits.user) {
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return [
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{
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role: "user",
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content: cannonball({
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input: user.content,
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targetTokenSize: llm.promptWindowLimit() * 0.8,
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tiktokenInstance: tokenManager,
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}),
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},
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];
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}
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const compressedSystem = new Promise(async (resolve) => {
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const count = tokenManager.countFromString(system.content);
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if (count < llm.limits.system) {
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resolve(system);
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return;
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}
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// Split context from system prompt - cannonball since its over the window.
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// We assume the context + user prompt is enough tokens to fit.
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const [prompt, context = ""] = system.content.split("Context:");
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let compressedPrompt;
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let compressedContext;
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// If the user system prompt contribution's to the system prompt is more than
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// 25% of the system limit, we will cannonball it - this favors the context
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// over the instruction from the user.
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if (tokenManager.countFromString(prompt) >= llm.limits.system * 0.25) {
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compressedPrompt = cannonball({
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input: prompt,
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targetTokenSize: llm.limits.system * 0.25,
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tiktokenInstance: tokenManager,
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});
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} else {
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compressedPrompt = prompt;
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}
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if (tokenManager.countFromString(context) >= llm.limits.system * 0.75) {
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compressedContext = cannonball({
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input: context,
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targetTokenSize: llm.limits.system * 0.75,
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tiktokenInstance: tokenManager,
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});
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} else {
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compressedContext = context;
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}
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system.content = `${compressedPrompt}${
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compressedContext ? `\nContext: ${compressedContext}` : ""
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}`;
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resolve(system);
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});
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// Prompt is allowed to take up to 70% of window - we know its under
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// if we are here, so passthrough.
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const compressedPrompt = new Promise(async (resolve) => resolve(user));
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// We always aggressively compress history because it is the least
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// important data to retain in full-fidelity.
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const compressedHistory = new Promise((resolve) => {
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const eligibleHistoryItems = [];
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var historyTokenCount = 0;
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for (const [i, history] of rawHistory.reverse().entries()) {
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const [user, assistant] = convertToPromptHistory([history]);
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const [userTokens, assistantTokens] = [
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tokenManager.countFromString(user.content),
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tokenManager.countFromString(assistant.content),
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];
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const total = userTokens + assistantTokens;
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// If during the loop the token cost of adding this history
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// is small, we can add it to history and move onto next.
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if (historyTokenCount + total < llm.limits.history) {
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eligibleHistoryItems.unshift(user, assistant);
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historyTokenCount += total;
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continue;
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}
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// If we reach here the overhead of adding this history item will
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// be too much of the limit. So now, we are prioritizing
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// the most recent 3 message pairs - if we are already past those - exit loop and stop
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// trying to make history work.
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if (i > 2) break;
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// We are over the limit and we are within the first 3 most recent chats.
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// so now we cannonball them to make them fit into the window.
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// max size = llm.limit.history; Each component of the message, can at most
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// be 50% of the history. We cannonball whichever is the problem.
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// The math isnt perfect for tokens, so we have to add a fudge factor for safety.
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const maxTargetSize = Math.floor(llm.limits.history / 2.2);
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if (userTokens > maxTargetSize) {
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user.content = cannonball({
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input: user.content,
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targetTokenSize: maxTargetSize,
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tiktokenInstance: tokenManager,
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});
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}
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if (assistantTokens > maxTargetSize) {
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assistant.content = cannonball({
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input: assistant.content,
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targetTokenSize: maxTargetSize,
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tiktokenInstance: tokenManager,
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});
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}
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const newTotal = tokenManager.statsFrom([user, assistant]);
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if (historyTokenCount + newTotal > llm.limits.history) continue;
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eligibleHistoryItems.unshift(user, assistant);
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historyTokenCount += newTotal;
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}
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resolve(eligibleHistoryItems);
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});
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const [cSystem, cHistory, cPrompt] = await Promise.all([
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compressedSystem,
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compressedHistory,
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compressedPrompt,
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]);
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return [cSystem, ...cHistory, cPrompt];
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}
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// Implementation of messageArrayCompressor, but for string only completion models
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async function messageStringCompressor(llm, promptArgs = {}, rawHistory = []) {
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const tokenBuffer = 600;
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const tokenManager = new TokenManager(llm.model);
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const initialPrompt = llm.constructPrompt(promptArgs);
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if (
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tokenManager.statsFrom(initialPrompt) + tokenBuffer <
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llm.promptWindowLimit()
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)
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return initialPrompt;
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const system = promptArgs.systemPrompt;
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const user = promptArgs.userPrompt;
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const userPromptSize = tokenManager.countFromString(user);
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// User prompt is the main focus here - we we prioritize it and allow
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// it to highjack the entire conversation thread. We are going to
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// cannonball the prompt through to ensure the reply has at least 20% of
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// the token supply to reply with.
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if (userPromptSize > llm.limits.user) {
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return llm.constructPrompt({
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userPrompt: cannonball({
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input: user,
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targetTokenSize: llm.promptWindowLimit() * 0.8,
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tiktokenInstance: tokenManager,
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}),
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});
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}
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const compressedSystem = new Promise(async (resolve) => {
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const count = tokenManager.countFromString(system);
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if (count < llm.limits.system) {
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resolve(system);
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return;
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}
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resolve(
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cannonball({
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input: system,
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targetTokenSize: llm.limits.system,
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tiktokenInstance: tokenManager,
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})
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);
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});
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// Prompt is allowed to take up to 70% of window - we know its under
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// if we are here, so passthrough.
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const compressedPrompt = new Promise(async (resolve) => resolve(user));
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// We always aggressively compress history because it is the least
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// important data to retain in full-fidelity.
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const compressedHistory = new Promise((resolve) => {
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const eligibleHistoryItems = [];
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var historyTokenCount = 0;
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for (const [i, history] of rawHistory.reverse().entries()) {
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const [user, assistant] = convertToPromptHistory([history]);
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const [userTokens, assistantTokens] = [
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tokenManager.countFromString(user.content),
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tokenManager.countFromString(assistant.content),
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];
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const total = userTokens + assistantTokens;
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// If during the loop the token cost of adding this history
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// is small, we can add it to history and move onto next.
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if (historyTokenCount + total < llm.limits.history) {
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eligibleHistoryItems.unshift(user, assistant);
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historyTokenCount += total;
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continue;
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}
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// If we reach here the overhead of adding this history item will
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// be too much of the limit. So now, we are prioritizing
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// the most recent 3 message pairs - if we are already past those - exit loop and stop
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// trying to make history work.
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if (i > 2) break;
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// We are over the limit and we are within the first 3 most recent chats.
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// so now we cannonball them to make them fit into the window.
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// max size = llm.limit.history; Each component of the message, can at most
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// be 50% of the history. We cannonball whichever is the problem.
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// The math isnt perfect for tokens, so we have to add a fudge factor for safety.
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const maxTargetSize = Math.floor(llm.limits.history / 2.2);
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if (userTokens > maxTargetSize) {
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user.content = cannonball({
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input: user.content,
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targetTokenSize: maxTargetSize,
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tiktokenInstance: tokenManager,
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});
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}
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if (assistantTokens > maxTargetSize) {
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assistant.content = cannonball({
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input: assistant.content,
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targetTokenSize: maxTargetSize,
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tiktokenInstance: tokenManager,
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});
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}
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const newTotal = tokenManager.statsFrom([user, assistant]);
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if (historyTokenCount + newTotal > llm.limits.history) continue;
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eligibleHistoryItems.unshift(user, assistant);
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historyTokenCount += newTotal;
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}
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resolve(eligibleHistoryItems);
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});
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const [cSystem, cHistory, cPrompt] = await Promise.all([
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compressedSystem,
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compressedHistory,
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compressedPrompt,
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]);
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return llm.constructPrompt({
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systemPrompt: cSystem,
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contextTexts: promptArgs?.contextTexts || [],
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chatHistory: cHistory,
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userPrompt: cPrompt,
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});
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}
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// Cannonball prompting: aka where we shoot a proportionally big cannonball through a proportional large prompt
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// Nobody should be sending prompts this big, but there is no reason we shouldn't allow it if results are good even by doing it.
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function cannonball({
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input = "",
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targetTokenSize = 0,
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tiktokenInstance = null,
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ellipsesStr = null,
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}) {
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if (!input || !targetTokenSize) return input;
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const tokenManager = tiktokenInstance || new TokenManager();
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const truncText = ellipsesStr || "\n\n--prompt truncated for brevity--\n\n";
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const initialInputSize = tokenManager.countFromString(input);
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if (initialInputSize < targetTokenSize) return input;
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// if the delta is the token difference between where our prompt is in size
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// and where we ideally need to land.
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const delta = initialInputSize - targetTokenSize;
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const tokenChunks = tokenManager.tokensFromString(input);
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const middleIdx = Math.floor(tokenChunks.length / 2);
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// middle truncate the text going left and right of midpoint
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const leftChunks = tokenChunks.slice(0, middleIdx - Math.round(delta / 2));
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const rightChunks = tokenChunks.slice(middleIdx + Math.round(delta / 2));
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const truncatedText =
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tokenManager.bytesFromTokens(leftChunks) +
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truncText +
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tokenManager.bytesFromTokens(rightChunks);
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console.log(
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`Cannonball results ${initialInputSize} -> ${tokenManager.countFromString(
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truncatedText
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)} tokens.`
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);
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return truncatedText;
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}
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/**
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* Fill the sources window with the priority of
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* 1. Pinned documents (handled prior to function)
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* 2. VectorSearch results
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* 3. prevSources in chat history - starting from most recent.
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*
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* Ensuring the window always has the desired amount of sources so that followup questions
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* in any chat mode have relevant sources, but not infinite sources. This function is used during chatting
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* and allows follow-up questions within a query chat that otherwise would have zero sources and would fail.
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* The added benefit is that during regular RAG chat, we have better coherence of citations that otherwise would
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* also yield no results with no need for a ReRanker to run and take much longer to return a response.
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*
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* The side effect of this is follow-up unrelated questions now have citations that would look totally irrelevant, however
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* we would rather optimize on the correctness of a response vs showing extraneous sources during a response. Given search
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* results always take a priority a good unrelated question that produces RAG results will still function as desired and due to previous
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* history backfill sources "changing context" mid-chat is handled appropriately.
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* example:
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* ---previous implementation---
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* prompt 1: "What is anythingllm?" -> possibly get 4 good sources
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* prompt 2: "Tell me some features" -> possible get 0 - 1 maybe relevant source + previous answer response -> bad response due to bad context mgmt
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* ---next implementation---
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* prompt 1: "What is anythingllm?" -> possibly get 4 good sources
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* prompt 2: "Tell me some features" -> possible get 0 - 1 maybe relevant source + previous answer response -> backfill with 3 good sources from previous -> much better response
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*
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* @param {Object} config - params to call
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* @param {object} config.nDocs = fill size of the window
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* @param {object} config.searchResults = vector similarityResponse results for .sources
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* @param {object[]} config.history - rawHistory of chat containing sources
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* @param {string[]} config.filterIdentifiers - Pinned document identifiers to prevent duplicate context
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* @returns {{
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* contextTexts: string[],
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* sources: object[],
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* }} - Array of sources that should be added to window
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*/
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function fillSourceWindow({
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nDocs = 4, // Number of documents
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searchResults = [], // Sources from similarity search
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history = [], // Raw history
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filterIdentifiers = [], // pinned document sources
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} = config) {
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const sources = [...searchResults];
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if (sources.length >= nDocs || history.length === 0) {
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return {
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sources,
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contextTexts: sources.map((src) => src.text),
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};
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}
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const log = (text, ...args) => {
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console.log(`\x1b[36m[fillSourceWindow]\x1b[0m ${text}`, ...args);
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};
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log(
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`Need to backfill ${nDocs - searchResults.length} chunks to fill in the source window for RAG!`
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);
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const seenChunks = new Set(searchResults.map((source) => source.id));
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// We need to reverse again because we need to iterate from bottom of array (most recent chats)
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// Looking at this function by itself you may think that this loop could be extreme for long history chats,
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// but this was already handled where `history` we derived. This comes from `recentChatHistory` which
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// includes a limit for history (default: 20). So this loop does not look as extreme as on first glance.
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for (const chat of history.reverse()) {
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if (sources.length >= nDocs) {
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log(
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`Citations backfilled to ${nDocs} references from ${searchResults.length} original citations.`
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);
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break;
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}
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const chatSources =
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safeJsonParse(chat.response, { sources: [] })?.sources || [];
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if (!chatSources?.length || !Array.isArray(chatSources)) continue;
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const validSources = chatSources.filter((source) => {
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return (
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filterIdentifiers.includes(sourceIdentifier(source)) == false && // source cannot be in current pins
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source.hasOwnProperty("score") && // source cannot have come from a pinned document that was previously pinned
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source.hasOwnProperty("text") && // source has a valid text property we can use
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seenChunks.has(source.id) == false // is unique
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);
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});
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for (const validSource of validSources) {
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if (sources.length >= nDocs) break;
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sources.push(validSource);
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seenChunks.add(validSource.id);
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}
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}
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return {
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sources,
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contextTexts: sources.map((src) => src.text),
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};
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}
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module.exports = {
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messageArrayCompressor,
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messageStringCompressor,
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fillSourceWindow,
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};
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