123 lines
5.2 KiB
JavaScript
123 lines
5.2 KiB
JavaScript
import { z } from "zod";
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import { interpolateFString, PromptTemplate } from "@langchain/core/prompts";
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import { MultiRouteChain } from "./multi_route.js";
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import { STRUCTURED_MULTI_PROMPT_ROUTER_TEMPLATE } from "./multi_prompt_prompt.js";
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import { LLMChain } from "../../chains/llm_chain.js";
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import { LLMRouterChain } from "./llm_router.js";
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import { ConversationChain } from "../../chains/conversation.js";
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import { zipEntries } from "./utils.js";
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import { RouterOutputParser } from "../../output_parsers/router.js";
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/**
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* A class that represents a multi-prompt chain in the LangChain
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* framework. It extends the MultiRouteChain class and provides additional
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* functionality specific to multi-prompt chains.
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* @example
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* ```typescript
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* const multiPromptChain = MultiPromptChain.fromLLMAndPrompts(new ChatOpenAI(), {
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* promptNames: ["physics", "math", "history"],
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* promptDescriptions: [
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* "Good for answering questions about physics",
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* "Good for answering math questions",
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* "Good for answering questions about history",
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* ],
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* promptTemplates: [
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* `You are a very smart physics professor. Here is a question:\n{input}\n`,
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* `You are a very good mathematician. Here is a question:\n{input}\n`,
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* `You are a very smart history professor. Here is a question:\n{input}\n`,
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* ],
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* });
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* const result = await multiPromptChain.call({
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* input: "What is the speed of light?",
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* });
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* ```
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*/
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export class MultiPromptChain extends MultiRouteChain {
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/**
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* @deprecated Use `fromLLMAndPrompts` instead
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*/
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static fromPrompts(llm, promptNames, promptDescriptions, promptTemplates, defaultChain, options) {
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return MultiPromptChain.fromLLMAndPrompts(llm, {
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promptNames,
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promptDescriptions,
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promptTemplates,
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defaultChain,
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multiRouteChainOpts: options,
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});
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}
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/**
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* A static method that creates an instance of MultiPromptChain from a
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* BaseLanguageModel and a set of prompts. It takes in optional parameters
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* for the default chain and additional options.
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* @param llm A BaseLanguageModel instance.
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* @param promptNames An array of prompt names.
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* @param promptDescriptions An array of prompt descriptions.
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* @param promptTemplates An array of prompt templates.
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* @param defaultChain An optional BaseChain instance to be used as the default chain.
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* @param llmChainOpts Optional parameters for the LLMChainInput, excluding 'llm' and 'prompt'.
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* @param conversationChainOpts Optional parameters for the LLMChainInput, excluding 'llm' and 'outputKey'.
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* @param multiRouteChainOpts Optional parameters for the MultiRouteChainInput, excluding 'defaultChain'.
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* @returns An instance of MultiPromptChain.
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*/
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static fromLLMAndPrompts(llm, { promptNames, promptDescriptions, promptTemplates, defaultChain, llmChainOpts, conversationChainOpts, multiRouteChainOpts, }) {
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const destinations = zipEntries(promptNames, promptDescriptions).map(([name, desc]) => `${name}: ${desc}`);
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const structuredOutputParserSchema = z.object({
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destination: z
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.string()
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.optional()
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.describe('name of the question answering system to use or "DEFAULT"'),
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next_inputs: z
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.object({
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input: z
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.string()
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.describe("a potentially modified version of the original input"),
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})
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.describe("input to be fed to the next model"),
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});
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const outputParser = new RouterOutputParser(structuredOutputParserSchema);
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const destinationsStr = destinations.join("\n");
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const routerTemplate = interpolateFString(STRUCTURED_MULTI_PROMPT_ROUTER_TEMPLATE(outputParser.getFormatInstructions({ interpolationDepth: 4 })), {
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destinations: destinationsStr,
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});
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const routerPrompt = new PromptTemplate({
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template: routerTemplate,
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inputVariables: ["input"],
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outputParser,
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});
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const routerChain = LLMRouterChain.fromLLM(llm, routerPrompt);
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const destinationChains = zipEntries(promptNames, promptTemplates).reduce((acc, [name, template]) => {
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let myPrompt;
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if (typeof template === "object") {
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myPrompt = template;
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}
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else if (typeof template === "string") {
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myPrompt = new PromptTemplate({
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template: template,
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inputVariables: ["input"],
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});
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}
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else {
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throw new Error("Invalid prompt template");
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}
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acc[name] = new LLMChain({
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...llmChainOpts,
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llm,
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prompt: myPrompt,
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});
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return acc;
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}, {});
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const convChain = new ConversationChain({
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...conversationChainOpts,
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llm,
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outputKey: "text",
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});
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return new MultiPromptChain({
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...multiRouteChainOpts,
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routerChain,
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destinationChains,
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defaultChain: defaultChain ?? convChain,
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});
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}
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_chainType() {
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return "multi_prompt_chain";
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}
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}
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