Merge branch 'ItzCrazyKns:master' into master
This commit is contained in:
commit
baef45b456
|
@ -6,7 +6,7 @@
|
|||
"scripts": {
|
||||
"start": "node dist/app.js",
|
||||
"build": "tsc",
|
||||
"dev": "nodemon src/app.ts" ,
|
||||
"dev": "nodemon src/app.ts",
|
||||
"format": "prettier . --check",
|
||||
"format:write": "prettier . --write"
|
||||
},
|
||||
|
|
|
@ -0,0 +1,55 @@
|
|||
import { RunnableSequence, RunnableMap } from '@langchain/core/runnables';
|
||||
import ListLineOutputParser from '../lib/outputParsers/listLineOutputParser';
|
||||
import { PromptTemplate } from '@langchain/core/prompts';
|
||||
import formatChatHistoryAsString from '../utils/formatHistory';
|
||||
import { BaseMessage } from '@langchain/core/messages';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
|
||||
const suggestionGeneratorPrompt = `
|
||||
You are an AI suggestion generator for an AI powered search engine. You will be given a conversation below. You need to generate 4-5 suggestions based on the conversation. The suggestion should be relevant to the conversation that can be used by the user to ask the chat model for more information.
|
||||
You need to make sure the suggestions are relevant to the conversation and are helpful to the user. Keep a note that the user might use these suggestions to ask a chat model for more information.
|
||||
Make sure the suggestions are medium in length and are informative and relevant to the conversation.
|
||||
|
||||
Provide these suggestions separated by newlines between the XML tags <suggestions> and </suggestions>. For example:
|
||||
|
||||
<suggestions>
|
||||
Suggestion 1
|
||||
Suggestion 2
|
||||
Suggestion 3
|
||||
</suggestions>
|
||||
|
||||
Conversation:
|
||||
{chat_history}
|
||||
`;
|
||||
|
||||
type SuggestionGeneratorInput = {
|
||||
chat_history: BaseMessage[];
|
||||
};
|
||||
|
||||
const outputParser = new ListLineOutputParser({
|
||||
key: 'suggestions',
|
||||
});
|
||||
|
||||
const createSuggestionGeneratorChain = (llm: BaseChatModel) => {
|
||||
return RunnableSequence.from([
|
||||
RunnableMap.from({
|
||||
chat_history: (input: SuggestionGeneratorInput) =>
|
||||
formatChatHistoryAsString(input.chat_history),
|
||||
}),
|
||||
PromptTemplate.fromTemplate(suggestionGeneratorPrompt),
|
||||
llm,
|
||||
outputParser,
|
||||
]);
|
||||
};
|
||||
|
||||
const generateSuggestions = (
|
||||
input: SuggestionGeneratorInput,
|
||||
llm: BaseChatModel,
|
||||
) => {
|
||||
(llm as ChatOpenAI).temperature = 0;
|
||||
const suggestionGeneratorChain = createSuggestionGeneratorChain(llm);
|
||||
return suggestionGeneratorChain.invoke(input);
|
||||
};
|
||||
|
||||
export default generateSuggestions;
|
|
@ -0,0 +1,43 @@
|
|||
import { BaseOutputParser } from '@langchain/core/output_parsers';
|
||||
|
||||
interface LineListOutputParserArgs {
|
||||
key?: string;
|
||||
}
|
||||
|
||||
class LineListOutputParser extends BaseOutputParser<string[]> {
|
||||
private key = 'questions';
|
||||
|
||||
constructor(args?: LineListOutputParserArgs) {
|
||||
super();
|
||||
this.key = args.key || this.key;
|
||||
}
|
||||
|
||||
static lc_name() {
|
||||
return 'LineListOutputParser';
|
||||
}
|
||||
|
||||
lc_namespace = ['langchain', 'output_parsers', 'line_list_output_parser'];
|
||||
|
||||
async parse(text: string): Promise<string[]> {
|
||||
const regex = /^(\s*(-|\*|\d+\.\s|\d+\)\s|\u2022)\s*)+/;
|
||||
const startKeyIndex = text.indexOf(`<${this.key}>`);
|
||||
const endKeyIndex = text.indexOf(`</${this.key}>`);
|
||||
const questionsStartIndex =
|
||||
startKeyIndex === -1 ? 0 : startKeyIndex + `<${this.key}>`.length;
|
||||
const questionsEndIndex = endKeyIndex === -1 ? text.length : endKeyIndex;
|
||||
const lines = text
|
||||
.slice(questionsStartIndex, questionsEndIndex)
|
||||
.trim()
|
||||
.split('\n')
|
||||
.filter((line) => line.trim() !== '')
|
||||
.map((line) => line.replace(regex, ''));
|
||||
|
||||
return lines;
|
||||
}
|
||||
|
||||
getFormatInstructions(): string {
|
||||
throw new Error('Not implemented.');
|
||||
}
|
||||
}
|
||||
|
||||
export default LineListOutputParser;
|
|
@ -157,7 +157,6 @@ export const getAvailableEmbeddingModelProviders = async () => {
|
|||
});
|
||||
return acc;
|
||||
}, {});
|
||||
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Ollama embeddings: ${err}`);
|
||||
}
|
||||
|
@ -172,10 +171,10 @@ export const getAvailableEmbeddingModelProviders = async () => {
|
|||
modelName: 'Xenova/gte-small',
|
||||
}),
|
||||
'Bert Multilingual': new HuggingFaceTransformersEmbeddings({
|
||||
modelName: 'Xenova/bert-base-multilingual-uncased'
|
||||
modelName: 'Xenova/bert-base-multilingual-uncased',
|
||||
}),
|
||||
};
|
||||
} catch(err) {
|
||||
} catch (err) {
|
||||
logger.error(`Error loading local embeddings: ${err}`);
|
||||
}
|
||||
|
||||
|
|
|
@ -3,6 +3,7 @@ import imagesRouter from './images';
|
|||
import videosRouter from './videos';
|
||||
import configRouter from './config';
|
||||
import modelsRouter from './models';
|
||||
import suggestionsRouter from './suggestions';
|
||||
|
||||
const router = express.Router();
|
||||
|
||||
|
@ -10,5 +11,6 @@ router.use('/images', imagesRouter);
|
|||
router.use('/videos', videosRouter);
|
||||
router.use('/config', configRouter);
|
||||
router.use('/models', modelsRouter);
|
||||
router.use('/suggestions', suggestionsRouter);
|
||||
|
||||
export default router;
|
||||
|
|
|
@ -0,0 +1,46 @@
|
|||
import express from 'express';
|
||||
import generateSuggestions from '../agents/suggestionGeneratorAgent';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { getAvailableChatModelProviders } from '../lib/providers';
|
||||
import { HumanMessage, AIMessage } from '@langchain/core/messages';
|
||||
import logger from '../utils/logger';
|
||||
|
||||
const router = express.Router();
|
||||
|
||||
router.post('/', async (req, res) => {
|
||||
try {
|
||||
let { chat_history, chat_model, chat_model_provider } = req.body;
|
||||
|
||||
chat_history = chat_history.map((msg: any) => {
|
||||
if (msg.role === 'user') {
|
||||
return new HumanMessage(msg.content);
|
||||
} else if (msg.role === 'assistant') {
|
||||
return new AIMessage(msg.content);
|
||||
}
|
||||
});
|
||||
|
||||
const chatModels = await getAvailableChatModelProviders();
|
||||
const provider = chat_model_provider || Object.keys(chatModels)[0];
|
||||
const chatModel = chat_model || Object.keys(chatModels[provider])[0];
|
||||
|
||||
let llm: BaseChatModel | undefined;
|
||||
|
||||
if (chatModels[provider] && chatModels[provider][chatModel]) {
|
||||
llm = chatModels[provider][chatModel] as BaseChatModel | undefined;
|
||||
}
|
||||
|
||||
if (!llm) {
|
||||
res.status(500).json({ message: 'Invalid LLM model selected' });
|
||||
return;
|
||||
}
|
||||
|
||||
const suggestions = await generateSuggestions({ chat_history }, llm);
|
||||
|
||||
res.status(200).json({ suggestions: suggestions });
|
||||
} catch (err) {
|
||||
res.status(500).json({ message: 'An error has occurred.' });
|
||||
logger.error(`Error in generating suggestions: ${err.message}`);
|
||||
}
|
||||
});
|
||||
|
||||
export default router;
|
Loading…
Reference in New Issue