282 lines
11 KiB
JavaScript
282 lines
11 KiB
JavaScript
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import { PromptTemplate } from "@langchain/core/prompts";
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import { HumanMessage, AIMessage } from "@langchain/core/messages";
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import { BaseChain } from "./base.js";
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import { LLMChain } from "./llm_chain.js";
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import { loadQAChain } from "./question_answering/load.js";
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const question_generator_template = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
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Chat History:
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{chat_history}
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Follow Up Input: {question}
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Standalone question:`;
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/**
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* @deprecated This class will be removed in 1.0.0. See below for an example implementation using
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* `createRetrievalChain`.
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*
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* Class for conducting conversational question-answering tasks with a
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* retrieval component. Extends the BaseChain class and implements the
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* ConversationalRetrievalQAChainInput interface.
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* @example
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* ```typescript
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* import { ChatAnthropic } from "@langchain/anthropic";
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* import {
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* ChatPromptTemplate,
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* MessagesPlaceholder,
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* } from "@langchain/core/prompts";
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* import { BaseMessage } from "@langchain/core/messages";
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* import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
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* import { createHistoryAwareRetriever } from "langchain/chains/history_aware_retriever";
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* import { createRetrievalChain } from "langchain/chains/retrieval";
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*
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* const retriever = ...your retriever;
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* const llm = new ChatAnthropic();
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*
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* // Contextualize question
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* const contextualizeQSystemPrompt = `
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* Given a chat history and the latest user question
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* which might reference context in the chat history,
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* formulate a standalone question which can be understood
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* without the chat history. Do NOT answer the question, just
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* reformulate it if needed and otherwise return it as is.`;
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* const contextualizeQPrompt = ChatPromptTemplate.fromMessages([
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* ["system", contextualizeQSystemPrompt],
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* new MessagesPlaceholder("chat_history"),
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* ["human", "{input}"],
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* ]);
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* const historyAwareRetriever = await createHistoryAwareRetriever({
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* llm,
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* retriever,
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* rephrasePrompt: contextualizeQPrompt,
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* });
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*
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* // Answer question
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* const qaSystemPrompt = `
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* You are an assistant for question-answering tasks. Use
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* the following pieces of retrieved context to answer the
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* question. If you don't know the answer, just say that you
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* don't know. Use three sentences maximum and keep the answer
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* concise.
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* \n\n
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* {context}`;
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* const qaPrompt = ChatPromptTemplate.fromMessages([
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* ["system", qaSystemPrompt],
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* new MessagesPlaceholder("chat_history"),
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* ["human", "{input}"],
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* ]);
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*
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* // Below we use createStuffDocuments_chain to feed all retrieved context
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* // into the LLM. Note that we can also use StuffDocumentsChain and other
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* // instances of BaseCombineDocumentsChain.
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* const questionAnswerChain = await createStuffDocumentsChain({
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* llm,
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* prompt: qaPrompt,
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* });
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*
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* const ragChain = await createRetrievalChain({
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* retriever: historyAwareRetriever,
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* combineDocsChain: questionAnswerChain,
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* });
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*
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* // Usage:
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* const chat_history: BaseMessage[] = [];
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* const response = await ragChain.invoke({
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* chat_history,
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* input: "...",
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* });
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* ```
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*/
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export class ConversationalRetrievalQAChain extends BaseChain {
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static lc_name() {
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return "ConversationalRetrievalQAChain";
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}
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get inputKeys() {
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return [this.inputKey, this.chatHistoryKey];
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}
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get outputKeys() {
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return this.combineDocumentsChain.outputKeys.concat(this.returnSourceDocuments ? ["sourceDocuments"] : []);
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}
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constructor(fields) {
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super(fields);
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Object.defineProperty(this, "inputKey", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: "question"
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});
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Object.defineProperty(this, "chatHistoryKey", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: "chat_history"
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});
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Object.defineProperty(this, "retriever", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "combineDocumentsChain", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "questionGeneratorChain", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "returnSourceDocuments", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: false
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});
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Object.defineProperty(this, "returnGeneratedQuestion", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: false
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});
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this.retriever = fields.retriever;
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this.combineDocumentsChain = fields.combineDocumentsChain;
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this.questionGeneratorChain = fields.questionGeneratorChain;
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this.inputKey = fields.inputKey ?? this.inputKey;
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this.returnSourceDocuments =
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fields.returnSourceDocuments ?? this.returnSourceDocuments;
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this.returnGeneratedQuestion =
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fields.returnGeneratedQuestion ?? this.returnGeneratedQuestion;
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}
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/**
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* Static method to convert the chat history input into a formatted
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* string.
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* @param chatHistory Chat history input which can be a string, an array of BaseMessage instances, or an array of string arrays.
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* @returns A formatted string representing the chat history.
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*/
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static getChatHistoryString(chatHistory) {
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let historyMessages;
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if (Array.isArray(chatHistory)) {
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// TODO: Deprecate on a breaking release
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if (Array.isArray(chatHistory[0]) &&
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typeof chatHistory[0][0] === "string") {
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console.warn("Passing chat history as an array of strings is deprecated.\nPlease see https://js.langchain.com/docs/modules/chains/popular/chat_vector_db#externally-managed-memory for more information.");
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historyMessages = chatHistory.flat().map((stringMessage, i) => {
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if (i % 2 === 0) {
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return new HumanMessage(stringMessage);
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}
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else {
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return new AIMessage(stringMessage);
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}
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});
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}
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else {
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historyMessages = chatHistory;
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}
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return historyMessages
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.map((chatMessage) => {
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if (chatMessage._getType() === "human") {
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return `Human: ${chatMessage.content}`;
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}
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else if (chatMessage._getType() === "ai") {
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return `Assistant: ${chatMessage.content}`;
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}
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else {
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return `${chatMessage.content}`;
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}
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})
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.join("\n");
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}
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return chatHistory;
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}
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/** @ignore */
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async _call(values, runManager) {
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if (!(this.inputKey in values)) {
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throw new Error(`Question key ${this.inputKey} not found.`);
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}
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if (!(this.chatHistoryKey in values)) {
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throw new Error(`Chat history key ${this.chatHistoryKey} not found.`);
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}
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const question = values[this.inputKey];
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const chatHistory = ConversationalRetrievalQAChain.getChatHistoryString(values[this.chatHistoryKey]);
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let newQuestion = question;
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if (chatHistory.length > 0) {
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const result = await this.questionGeneratorChain.call({
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question,
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chat_history: chatHistory,
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}, runManager?.getChild("question_generator"));
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const keys = Object.keys(result);
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if (keys.length === 1) {
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newQuestion = result[keys[0]];
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}
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else {
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throw new Error("Return from llm chain has multiple values, only single values supported.");
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}
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}
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const docs = await this.retriever.getRelevantDocuments(newQuestion, runManager?.getChild("retriever"));
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const inputs = {
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question: newQuestion,
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input_documents: docs,
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chat_history: chatHistory,
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};
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let result = await this.combineDocumentsChain.call(inputs, runManager?.getChild("combine_documents"));
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if (this.returnSourceDocuments) {
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result = {
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...result,
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sourceDocuments: docs,
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};
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}
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if (this.returnGeneratedQuestion) {
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result = {
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...result,
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generatedQuestion: newQuestion,
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};
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}
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return result;
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}
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_chainType() {
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return "conversational_retrieval_chain";
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}
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static async deserialize(_data, _values) {
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throw new Error("Not implemented.");
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}
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serialize() {
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throw new Error("Not implemented.");
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}
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/**
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* Static method to create a new ConversationalRetrievalQAChain from a
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* BaseLanguageModel and a BaseRetriever.
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* @param llm {@link BaseLanguageModelInterface} instance used to generate a new question.
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* @param retriever {@link BaseRetrieverInterface} instance used to retrieve relevant documents.
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* @param options.returnSourceDocuments Whether to return source documents in the final output
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* @param options.questionGeneratorChainOptions Options to initialize the standalone question generation chain used as the first internal step
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* @param options.qaChainOptions {@link QAChainParams} used to initialize the QA chain used as the second internal step
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* @returns A new instance of ConversationalRetrievalQAChain.
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*/
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static fromLLM(llm, retriever, options = {}) {
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const { questionGeneratorTemplate, qaTemplate, qaChainOptions = {
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type: "stuff",
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prompt: qaTemplate
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? PromptTemplate.fromTemplate(qaTemplate)
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: undefined,
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}, questionGeneratorChainOptions, verbose, ...rest } = options;
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const qaChain = loadQAChain(llm, qaChainOptions);
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const questionGeneratorChainPrompt = PromptTemplate.fromTemplate(questionGeneratorChainOptions?.template ??
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questionGeneratorTemplate ??
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question_generator_template);
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const questionGeneratorChain = new LLMChain({
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prompt: questionGeneratorChainPrompt,
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llm: questionGeneratorChainOptions?.llm ?? llm,
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verbose,
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});
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const instance = new this({
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retriever,
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combineDocumentsChain: qaChain,
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questionGeneratorChain,
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verbose,
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...rest,
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});
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return instance;
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}
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}
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