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ChatEngine.ts
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ChatEngine.ts
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import { v4 as uuidv4 } from "uuid";
import { Event } from "./callbacks/CallbackManager";
import { ChatHistory } from "./ChatHistory";
import { BaseNodePostprocessor } from "./indices/BaseNodePostprocessor";
import { ChatMessage, LLM, OpenAI } from "./llm/LLM";
import { NodeWithScore, TextNode } from "./Node";
import {
CondenseQuestionPrompt,
ContextSystemPrompt,
defaultCondenseQuestionPrompt,
defaultContextSystemPrompt,
messagesToHistoryStr,
} from "./Prompt";
import { BaseQueryEngine } from "./QueryEngine";
import { Response } from "./Response";
import { BaseRetriever } from "./Retriever";
import { ServiceContext, serviceContextFromDefaults } from "./ServiceContext";
/**
* A ChatEngine is used to handle back and forth chats between the application and the LLM.
*/
export interface ChatEngine {
/**
* Send message along with the class's current chat history to the LLM.
* @param message
* @param chatHistory optional chat history if you want to customize the chat history
* @param streaming optional streaming flag, which auto-sets the return value if True.
*/
chat<
T extends boolean | undefined = undefined,
R = T extends true ? AsyncGenerator<string, void, unknown> : Response,
>(
message: string,
chatHistory?: ChatMessage[],
streaming?: T,
): Promise<R>;
/**
* Resets the chat history so that it's empty.
*/
reset(): void;
}
/**
* SimpleChatEngine is the simplest possible chat engine. Useful for using your own custom prompts.
*/
export class SimpleChatEngine implements ChatEngine {
chatHistory: ChatMessage[];
llm: LLM;
constructor(init?: Partial<SimpleChatEngine>) {
this.chatHistory = init?.chatHistory ?? [];
this.llm = init?.llm ?? new OpenAI();
}
async chat<
T extends boolean | undefined = undefined,
R = T extends true ? AsyncGenerator<string, void, unknown> : Response,
>(message: string, chatHistory?: ChatMessage[], streaming?: T): Promise<R> {
//Streaming option
if (streaming) {
return this.streamChat(message, chatHistory) as R;
}
//Non-streaming option
chatHistory = chatHistory ?? this.chatHistory;
chatHistory.push({ content: message, role: "user" });
const response = await this.llm.chat(chatHistory, undefined);
chatHistory.push(response.message);
this.chatHistory = chatHistory;
return new Response(response.message.content) as R;
}
protected async *streamChat(
message: string,
chatHistory?: ChatMessage[],
): AsyncGenerator<string, void, unknown> {
chatHistory = chatHistory ?? this.chatHistory;
chatHistory.push({ content: message, role: "user" });
const response_generator = await this.llm.chat(
chatHistory,
undefined,
true,
);
var accumulator: string = "";
for await (const part of response_generator) {
accumulator += part;
yield part;
}
chatHistory.push({ content: accumulator, role: "assistant" });
this.chatHistory = chatHistory;
return;
}
reset() {
this.chatHistory = [];
}
}
/**
* CondenseQuestionChatEngine is used in conjunction with a Index (for example VectorStoreIndex).
* It does two steps on taking a user's chat message: first, it condenses the chat message
* with the previous chat history into a question with more context.
* Then, it queries the underlying Index using the new question with context and returns
* the response.
* CondenseQuestionChatEngine performs well when the input is primarily questions about the
* underlying data. It performs less well when the chat messages are not questions about the
* data, or are very referential to previous context.
*/
export class CondenseQuestionChatEngine implements ChatEngine {
queryEngine: BaseQueryEngine;
chatHistory: ChatMessage[];
serviceContext: ServiceContext;
condenseMessagePrompt: CondenseQuestionPrompt;
constructor(init: {
queryEngine: BaseQueryEngine;
chatHistory: ChatMessage[];
serviceContext?: ServiceContext;
condenseMessagePrompt?: CondenseQuestionPrompt;
}) {
this.queryEngine = init.queryEngine;
this.chatHistory = init?.chatHistory ?? [];
this.serviceContext =
init?.serviceContext ?? serviceContextFromDefaults({});
this.condenseMessagePrompt =
init?.condenseMessagePrompt ?? defaultCondenseQuestionPrompt;
}
private async condenseQuestion(chatHistory: ChatMessage[], question: string) {
const chatHistoryStr = messagesToHistoryStr(chatHistory);
return this.serviceContext.llm.complete(
defaultCondenseQuestionPrompt({
question: question,
chatHistory: chatHistoryStr,
}),
);
}
async chat<
T extends boolean | undefined = undefined,
R = T extends true ? AsyncGenerator<string, void, unknown> : Response,
>(
message: string,
chatHistory?: ChatMessage[] | undefined,
streaming?: T,
): Promise<R> {
chatHistory = chatHistory ?? this.chatHistory;
const condensedQuestion = (
await this.condenseQuestion(chatHistory, message)
).message.content;
const response = await this.queryEngine.query(condensedQuestion);
chatHistory.push({ content: message, role: "user" });
chatHistory.push({ content: response.response, role: "assistant" });
return response as R;
}
reset() {
this.chatHistory = [];
}
}
export interface Context {
message: ChatMessage;
nodes: NodeWithScore[];
}
export interface ContextGenerator {
generate(message: string, parentEvent?: Event): Promise<Context>;
}
export class DefaultContextGenerator implements ContextGenerator {
retriever: BaseRetriever;
contextSystemPrompt: ContextSystemPrompt;
nodePostprocessors: BaseNodePostprocessor[];
constructor(init: {
retriever: BaseRetriever;
contextSystemPrompt?: ContextSystemPrompt;
nodePostprocessors?: BaseNodePostprocessor[];
}) {
this.retriever = init.retriever;
this.contextSystemPrompt =
init?.contextSystemPrompt ?? defaultContextSystemPrompt;
this.nodePostprocessors = init.nodePostprocessors || [];
}
private applyNodePostprocessors(nodes: NodeWithScore[]) {
return this.nodePostprocessors.reduce(
(nodes, nodePostprocessor) => nodePostprocessor.postprocessNodes(nodes),
nodes,
);
}
async generate(message: string, parentEvent?: Event): Promise<Context> {
if (!parentEvent) {
parentEvent = {
id: uuidv4(),
type: "wrapper",
tags: ["final"],
};
}
const sourceNodesWithScore = await this.retriever.retrieve(
message,
parentEvent,
);
const nodes = this.applyNodePostprocessors(sourceNodesWithScore);
return {
message: {
content: this.contextSystemPrompt({
context: nodes.map((r) => (r.node as TextNode).text).join("\n\n"),
}),
role: "system",
},
nodes,
};
}
}
/**
* ContextChatEngine uses the Index to get the appropriate context for each query.
* The context is stored in the system prompt, and the chat history is preserved,
* ideally allowing the appropriate context to be surfaced for each query.
*/
export class ContextChatEngine implements ChatEngine {
chatModel: LLM;
chatHistory: ChatMessage[];
contextGenerator: ContextGenerator;
constructor(init: {
retriever: BaseRetriever;
chatModel?: LLM;
chatHistory?: ChatMessage[];
contextSystemPrompt?: ContextSystemPrompt;
nodePostprocessors?: BaseNodePostprocessor[];
}) {
this.chatModel =
init.chatModel ?? new OpenAI({ model: "gpt-3.5-turbo-16k" });
this.chatHistory = init?.chatHistory ?? [];
this.contextGenerator = new DefaultContextGenerator({
retriever: init.retriever,
contextSystemPrompt: init?.contextSystemPrompt,
});
}
async chat<
T extends boolean | undefined = undefined,
R = T extends true ? AsyncGenerator<string, void, unknown> : Response,
>(
message: string,
chatHistory?: ChatMessage[] | undefined,
streaming?: T,
): Promise<R> {
chatHistory = chatHistory ?? this.chatHistory;
//Streaming option
if (streaming) {
return this.streamChat(message, chatHistory) as R;
}
const parentEvent: Event = {
id: uuidv4(),
type: "wrapper",
tags: ["final"],
};
const context = await this.contextGenerator.generate(message, parentEvent);
chatHistory.push({ content: message, role: "user" });
const response = await this.chatModel.chat(
[context.message, ...chatHistory],
parentEvent,
);
chatHistory.push(response.message);
this.chatHistory = chatHistory;
return new Response(
response.message.content,
context.nodes.map((r) => r.node),
) as R;
}
protected async *streamChat(
message: string,
chatHistory?: ChatMessage[] | undefined,
): AsyncGenerator<string, void, unknown> {
chatHistory = chatHistory ?? this.chatHistory;
const parentEvent: Event = {
id: uuidv4(),
type: "wrapper",
tags: ["final"],
};
const context = await this.contextGenerator.generate(message, parentEvent);
chatHistory.push({ content: message, role: "user" });
const response_stream = await this.chatModel.chat(
[context.message, ...chatHistory],
parentEvent,
true,
);
var accumulator: string = "";
for await (const part of response_stream) {
accumulator += part;
yield part;
}
chatHistory.push({ content: accumulator, role: "assistant" });
this.chatHistory = chatHistory;
return;
}
reset() {
this.chatHistory = [];
}
}
/**
* HistoryChatEngine is a ChatEngine that uses a `ChatHistory` object
* to keeps track of chat's message history.
* A `ChatHistory` object is passed as a parameter for each call to the `chat` method,
* so the state of the chat engine is preserved between calls.
* Optionally, a `ContextGenerator` can be used to generate an additional context for each call to `chat`.
*/
export class HistoryChatEngine {
llm: LLM;
contextGenerator?: ContextGenerator;
constructor(init?: Partial<HistoryChatEngine>) {
this.llm = init?.llm ?? new OpenAI();
this.contextGenerator = init?.contextGenerator;
}
async chat<
T extends boolean | undefined = undefined,
R = T extends true ? AsyncGenerator<string, void, unknown> : Response,
>(message: string, chatHistory: ChatHistory, streaming?: T): Promise<R> {
//Streaming option
if (streaming) {
return this.streamChat(message, chatHistory) as R;
}
const context = await this.contextGenerator?.generate(message);
chatHistory.addMessage({
content: message,
role: "user",
});
const response = await this.llm.chat(
await chatHistory.requestMessages(
context ? [context.message] : undefined,
),
);
chatHistory.addMessage(response.message);
return new Response(response.message.content) as R;
}
protected async *streamChat(
message: string,
chatHistory: ChatHistory,
): AsyncGenerator<string, void, unknown> {
const context = await this.contextGenerator?.generate(message);
chatHistory.addMessage({
content: message,
role: "user",
});
const response_stream = await this.llm.chat(
await chatHistory.requestMessages(
context ? [context.message] : undefined,
),
undefined,
true,
);
var accumulator = "";
for await (const part of response_stream) {
accumulator += part;
yield part;
}
chatHistory.addMessage({
content: accumulator,
role: "assistant",
});
return;
}
}