mirror of
https://github.com/ItzCrazyKns/Perplexica.git
synced 2025-04-29 07:42:46 +00:00
feat(app): remove backend
This commit is contained in:
ItzCrazyKns
6
.gitignore
vendored
6
.gitignore
vendored
@ -4,9 +4,9 @@ npm-debug.log
|
||||
yarn-error.log
|
||||
|
||||
# Build output
|
||||
/.next/
|
||||
/out/
|
||||
/dist/
|
||||
.next/
|
||||
out/
|
||||
dist/
|
||||
|
||||
# IDE/Editor specific
|
||||
.vscode/
|
||||
|
||||
@ -6,7 +6,6 @@ const config = {
|
||||
endOfLine: 'auto',
|
||||
singleQuote: true,
|
||||
tabWidth: 2,
|
||||
semi: true,
|
||||
};
|
||||
|
||||
module.exports = config;
|
||||
|
||||
@ -1,13 +1,20 @@
|
||||
FROM node:20.18.0-alpine
|
||||
|
||||
ARG NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
|
||||
ARG NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
|
||||
ENV NEXT_PUBLIC_WS_URL=${NEXT_PUBLIC_WS_URL}
|
||||
ENV NEXT_PUBLIC_API_URL=${NEXT_PUBLIC_API_URL}
|
||||
|
||||
WORKDIR /home/perplexica
|
||||
|
||||
COPY ui /home/perplexica/
|
||||
COPY src /home/perplexica/src
|
||||
COPY public /home/perplexica/public
|
||||
COPY package.json /home/perplexica/package.json
|
||||
COPY yarn.lock /home/perplexica/yarn.lock
|
||||
COPY tsconfig.json /home/perplexica/tsconfig.json
|
||||
COPY next.config.mjs /home/perplexica/next.config.mjs
|
||||
COPY next-env.d.ts /home/perplexica/next-env.d.ts
|
||||
COPY postcss.config.js /home/perplexica/postcss.config.js
|
||||
COPY drizzle.config.ts /home/perplexica/drizzle.config.ts
|
||||
COPY tailwind.config.ts /home/perplexica/tailwind.config.ts
|
||||
|
||||
RUN mkdir /home/perplexica/data
|
||||
RUN mkdir /home/perplexica/uploads
|
||||
|
||||
RUN yarn install --frozen-lockfile
|
||||
RUN yarn build
|
||||
|
||||
@ -1,17 +0,0 @@
|
||||
FROM node:18-slim
|
||||
|
||||
WORKDIR /home/perplexica
|
||||
|
||||
COPY src /home/perplexica/src
|
||||
COPY tsconfig.json /home/perplexica/
|
||||
COPY drizzle.config.ts /home/perplexica/
|
||||
COPY package.json /home/perplexica/
|
||||
COPY yarn.lock /home/perplexica/
|
||||
|
||||
RUN mkdir /home/perplexica/data
|
||||
RUN mkdir /home/perplexica/uploads
|
||||
|
||||
RUN yarn install --frozen-lockfile --network-timeout 600000
|
||||
RUN yarn build
|
||||
|
||||
CMD ["yarn", "start"]
|
||||
@ -9,41 +9,20 @@ services:
|
||||
- perplexica-network
|
||||
restart: unless-stopped
|
||||
|
||||
perplexica-backend:
|
||||
build:
|
||||
context: .
|
||||
dockerfile: backend.dockerfile
|
||||
image: itzcrazykns1337/perplexica-backend:main
|
||||
environment:
|
||||
- SEARXNG_API_URL=http://searxng:8080
|
||||
depends_on:
|
||||
- searxng
|
||||
ports:
|
||||
- 3001:3001
|
||||
volumes:
|
||||
- backend-dbstore:/home/perplexica/data
|
||||
- uploads:/home/perplexica/uploads
|
||||
- ./config.toml:/home/perplexica/config.toml
|
||||
extra_hosts:
|
||||
- 'host.docker.internal:host-gateway'
|
||||
networks:
|
||||
- perplexica-network
|
||||
restart: unless-stopped
|
||||
|
||||
perplexica-frontend:
|
||||
app:
|
||||
build:
|
||||
context: .
|
||||
dockerfile: app.dockerfile
|
||||
args:
|
||||
- NEXT_PUBLIC_API_URL=http://127.0.0.1:3001/api
|
||||
- NEXT_PUBLIC_WS_URL=ws://127.0.0.1:3001
|
||||
image: itzcrazykns1337/perplexica-frontend:main
|
||||
depends_on:
|
||||
- perplexica-backend
|
||||
environment:
|
||||
- SEARXNG_API_URL=http://searxng:8080
|
||||
ports:
|
||||
- 3000:3000
|
||||
networks:
|
||||
- perplexica-network
|
||||
volumes:
|
||||
- backend-dbstore:/home/perplexica/data
|
||||
- uploads:/home/perplexica/uploads
|
||||
- ./config.toml:/home/perplexica/config.toml
|
||||
restart: unless-stopped
|
||||
|
||||
networks:
|
||||
|
||||
@ -2,7 +2,7 @@ import { defineConfig } from 'drizzle-kit';
|
||||
|
||||
export default defineConfig({
|
||||
dialect: 'sqlite',
|
||||
schema: './src/db/schema.ts',
|
||||
schema: './src/lib/db/schema.ts',
|
||||
out: './drizzle',
|
||||
dbCredentials: {
|
||||
url: './data/db.sqlite',
|
||||
|
||||
5
next-env.d.ts
vendored
Normal file
5
next-env.d.ts
vendored
Normal file
@ -0,0 +1,5 @@
|
||||
/// <reference types="next" />
|
||||
/// <reference types="next/image-types/global" />
|
||||
|
||||
// NOTE: This file should not be edited
|
||||
// see https://nextjs.org/docs/app/api-reference/config/typescript for more information.
|
||||
81
package.json
81
package.json
@ -1,53 +1,62 @@
|
||||
{
|
||||
"name": "perplexica-backend",
|
||||
"name": "perplexica-frontend",
|
||||
"version": "1.10.0-rc3",
|
||||
"license": "MIT",
|
||||
"author": "ItzCrazyKns",
|
||||
"scripts": {
|
||||
"start": "npm run db:push && node dist/app.js",
|
||||
"build": "tsc",
|
||||
"dev": "nodemon --ignore uploads/ src/app.ts ",
|
||||
"db:push": "drizzle-kit push sqlite",
|
||||
"format": "prettier . --check",
|
||||
"format:write": "prettier . --write"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@types/better-sqlite3": "^7.6.10",
|
||||
"@types/cors": "^2.8.17",
|
||||
"@types/express": "^4.17.21",
|
||||
"@types/html-to-text": "^9.0.4",
|
||||
"@types/multer": "^1.4.12",
|
||||
"@types/pdf-parse": "^1.1.4",
|
||||
"@types/readable-stream": "^4.0.11",
|
||||
"@types/ws": "^8.5.12",
|
||||
"drizzle-kit": "^0.22.7",
|
||||
"nodemon": "^3.1.0",
|
||||
"prettier": "^3.2.5",
|
||||
"ts-node": "^10.9.2",
|
||||
"typescript": "^5.4.3"
|
||||
"dev": "next dev",
|
||||
"build": "next build",
|
||||
"start": "next start",
|
||||
"lint": "next lint",
|
||||
"format:write": "prettier . --write",
|
||||
"db:push": "drizzle-kit push sqlite"
|
||||
},
|
||||
"dependencies": {
|
||||
"@headlessui/react": "^2.2.0",
|
||||
"@iarna/toml": "^2.2.5",
|
||||
"@langchain/anthropic": "^0.2.3",
|
||||
"@langchain/community": "^0.2.16",
|
||||
"@icons-pack/react-simple-icons": "^12.3.0",
|
||||
"@langchain/community": "^0.3.36",
|
||||
"@langchain/core": "^0.3.42",
|
||||
"@langchain/openai": "^0.0.25",
|
||||
"@langchain/google-genai": "^0.0.23",
|
||||
"@xenova/transformers": "^2.17.1",
|
||||
"axios": "^1.6.8",
|
||||
"better-sqlite3": "^11.0.0",
|
||||
"@langchain/textsplitters": "^0.1.0",
|
||||
"@tailwindcss/typography": "^0.5.12",
|
||||
"axios": "^1.8.3",
|
||||
"better-sqlite3": "^11.9.1",
|
||||
"clsx": "^2.1.0",
|
||||
"compute-cosine-similarity": "^1.1.0",
|
||||
"compute-dot": "^1.1.0",
|
||||
"cors": "^2.8.5",
|
||||
"dotenv": "^16.4.5",
|
||||
"drizzle-orm": "^0.31.2",
|
||||
"express": "^4.19.2",
|
||||
"drizzle-orm": "^0.40.1",
|
||||
"html-to-text": "^9.0.5",
|
||||
"langchain": "^0.1.30",
|
||||
"mammoth": "^1.8.0",
|
||||
"multer": "^1.4.5-lts.1",
|
||||
"lucide-react": "^0.363.0",
|
||||
"markdown-to-jsx": "^7.7.2",
|
||||
"next": "^15.2.2",
|
||||
"next-themes": "^0.3.0",
|
||||
"pdf-parse": "^1.1.1",
|
||||
"winston": "^3.13.0",
|
||||
"ws": "^8.17.1",
|
||||
"react": "^18",
|
||||
"react-dom": "^18",
|
||||
"react-text-to-speech": "^0.14.5",
|
||||
"react-textarea-autosize": "^8.5.3",
|
||||
"sonner": "^1.4.41",
|
||||
"tailwind-merge": "^2.2.2",
|
||||
"winston": "^3.17.0",
|
||||
"yet-another-react-lightbox": "^3.17.2",
|
||||
"zod": "^3.22.4"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@types/better-sqlite3": "^7.6.12",
|
||||
"@types/html-to-text": "^9.0.4",
|
||||
"@types/node": "^20",
|
||||
"@types/pdf-parse": "^1.1.4",
|
||||
"@types/react": "^18",
|
||||
"@types/react-dom": "^18",
|
||||
"autoprefixer": "^10.0.1",
|
||||
"drizzle-kit": "^0.30.5",
|
||||
"eslint": "^8",
|
||||
"eslint-config-next": "14.1.4",
|
||||
"postcss": "^8",
|
||||
"prettier": "^3.2.5",
|
||||
"tailwindcss": "^3.3.0",
|
||||
"typescript": "^5"
|
||||
}
|
||||
}
|
||||
|
||||
|
Before Width: | Height: | Size: 1.3 KiB After Width: | Height: | Size: 1.3 KiB |
|
Before Width: | Height: | Size: 629 B After Width: | Height: | Size: 629 B |
@ -24,4 +24,4 @@ MODEL_NAME = ""
|
||||
API_URL = "" # Ollama API URL - http://host.docker.internal:11434
|
||||
|
||||
[API_ENDPOINTS]
|
||||
SEARXNG = "http://localhost:32768" # SearxNG API URL
|
||||
SEARXNG = "" # SearxNG API URL - http://localhost:32768
|
||||
38
src/app.ts
38
src/app.ts
@ -1,38 +0,0 @@
|
||||
import { startWebSocketServer } from './websocket';
|
||||
import express from 'express';
|
||||
import cors from 'cors';
|
||||
import http from 'http';
|
||||
import routes from './routes';
|
||||
import { getPort } from './config';
|
||||
import logger from './utils/logger';
|
||||
|
||||
const port = getPort();
|
||||
|
||||
const app = express();
|
||||
const server = http.createServer(app);
|
||||
|
||||
const corsOptions = {
|
||||
origin: '*',
|
||||
};
|
||||
|
||||
app.use(cors(corsOptions));
|
||||
app.use(express.json());
|
||||
|
||||
app.use('/api', routes);
|
||||
app.get('/api', (_, res) => {
|
||||
res.status(200).json({ status: 'ok' });
|
||||
});
|
||||
|
||||
server.listen(port, () => {
|
||||
logger.info(`Server is running on port ${port}`);
|
||||
});
|
||||
|
||||
startWebSocketServer(server);
|
||||
|
||||
process.on('uncaughtException', (err, origin) => {
|
||||
logger.error(`Uncaught Exception at ${origin}: ${err}`);
|
||||
});
|
||||
|
||||
process.on('unhandledRejection', (reason, promise) => {
|
||||
logger.error(`Unhandled Rejection at: ${promise}, reason: ${reason}`);
|
||||
});
|
||||
@ -3,16 +3,28 @@ import MetaSearchAgent from '@/lib/search/metaSearchAgent';
|
||||
import crypto from 'crypto';
|
||||
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
|
||||
import { EventEmitter } from 'stream';
|
||||
import { chatModelProviders, embeddingModelProviders } from '@/lib/providers';
|
||||
import {
|
||||
chatModelProviders,
|
||||
embeddingModelProviders,
|
||||
getAvailableChatModelProviders,
|
||||
getAvailableEmbeddingModelProviders,
|
||||
} from '@/lib/providers';
|
||||
import db from '@/lib/db';
|
||||
import { chats, messages as messagesSchema } from '@/lib/db/schema';
|
||||
import { and, eq, gt } from 'drizzle-orm';
|
||||
import { getFileDetails } from '@/lib/utils/files';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
import {
|
||||
getCustomOpenaiApiKey,
|
||||
getCustomOpenaiApiUrl,
|
||||
getCustomOpenaiModelName,
|
||||
} from '@/lib/config';
|
||||
|
||||
export const runtime = 'nodejs';
|
||||
export const dynamic = 'force-dynamic';
|
||||
|
||||
export const searchHandlers: Record<string, MetaSearchAgent> = {
|
||||
const searchHandlers: Record<string, MetaSearchAgent> = {
|
||||
webSearch: new MetaSearchAgent({
|
||||
activeEngines: [],
|
||||
queryGeneratorPrompt: prompts.webSearchRetrieverPrompt,
|
||||
@ -227,7 +239,7 @@ const handleHistorySave = async (
|
||||
export const POST = async (req: Request) => {
|
||||
try {
|
||||
const body = (await req.json()) as Body;
|
||||
const { message, chatModel, embeddingModel } = body;
|
||||
const { message } = body;
|
||||
|
||||
if (message.content === '') {
|
||||
return Response.json(
|
||||
@ -238,50 +250,52 @@ export const POST = async (req: Request) => {
|
||||
);
|
||||
}
|
||||
|
||||
const getProviderChatModels = chatModelProviders[chatModel.provider];
|
||||
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
|
||||
getAvailableChatModelProviders(),
|
||||
getAvailableEmbeddingModelProviders(),
|
||||
]);
|
||||
|
||||
if (!getProviderChatModels) {
|
||||
return Response.json(
|
||||
{
|
||||
message: 'Invalid chat model provider',
|
||||
const chatModelProvider =
|
||||
chatModelProviders[
|
||||
body.chatModel?.provider || Object.keys(chatModelProviders)[0]
|
||||
];
|
||||
const chatModel =
|
||||
chatModelProvider[
|
||||
body.chatModel?.name || Object.keys(chatModelProvider)[0]
|
||||
];
|
||||
|
||||
const embeddingProvider =
|
||||
embeddingModelProviders[
|
||||
body.embeddingModel?.provider || Object.keys(embeddingModelProviders)[0]
|
||||
];
|
||||
const embeddingModel =
|
||||
embeddingProvider[
|
||||
body.embeddingModel?.name || Object.keys(embeddingProvider)[0]
|
||||
];
|
||||
|
||||
let llm: BaseChatModel | undefined;
|
||||
let embedding = embeddingModel.model;
|
||||
|
||||
if (body.chatModel?.provider === 'custom_openai') {
|
||||
llm = new ChatOpenAI({
|
||||
openAIApiKey: getCustomOpenaiApiKey(),
|
||||
modelName: getCustomOpenaiModelName(),
|
||||
temperature: 0.7,
|
||||
configuration: {
|
||||
baseURL: getCustomOpenaiApiUrl(),
|
||||
},
|
||||
{ status: 400 },
|
||||
);
|
||||
}) as unknown as BaseChatModel;
|
||||
} else if (chatModelProvider && chatModel) {
|
||||
llm = chatModel.model;
|
||||
}
|
||||
|
||||
const chatModels = await getProviderChatModels();
|
||||
|
||||
const llm = chatModels[chatModel.name].model;
|
||||
|
||||
if (!llm) {
|
||||
return Response.json(
|
||||
{
|
||||
message: 'Invalid chat model',
|
||||
},
|
||||
{ status: 400 },
|
||||
);
|
||||
return Response.json({ error: 'Invalid chat model' }, { status: 400 });
|
||||
}
|
||||
|
||||
const getProviderEmbeddingModels =
|
||||
embeddingModelProviders[embeddingModel.provider];
|
||||
|
||||
if (!getProviderEmbeddingModels) {
|
||||
return Response.json(
|
||||
{
|
||||
message: 'Invalid embedding model provider',
|
||||
},
|
||||
{ status: 400 },
|
||||
);
|
||||
}
|
||||
|
||||
const embeddingModels = await getProviderEmbeddingModels();
|
||||
const embedding = embeddingModels[embeddingModel.name].model;
|
||||
|
||||
if (!embedding) {
|
||||
return Response.json(
|
||||
{
|
||||
message: 'Invalid embedding model',
|
||||
},
|
||||
{ error: 'Invalid embedding model' },
|
||||
{ status: 400 },
|
||||
);
|
||||
}
|
||||
@ -6,7 +6,7 @@ import {
|
||||
} from '@/lib/config';
|
||||
import { getAvailableChatModelProviders } from '@/lib/providers';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { AIMessage, HumanMessage } from '@langchain/core/messages';
|
||||
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
|
||||
interface ChatModel {
|
||||
@ -32,7 +32,7 @@ export const POST = async (req: Request) => {
|
||||
return new AIMessage(msg.content);
|
||||
}
|
||||
})
|
||||
.filter((msg) => msg !== undefined);
|
||||
.filter((msg) => msg !== undefined) as BaseMessage[];
|
||||
|
||||
const chatModelProviders = await getAvailableChatModelProviders();
|
||||
|
||||
@ -55,7 +55,7 @@ export const POST = async (req: Request) => {
|
||||
configuration: {
|
||||
baseURL: getCustomOpenaiApiUrl(),
|
||||
},
|
||||
});
|
||||
}) as unknown as BaseChatModel;
|
||||
} else if (chatModelProvider && chatModel) {
|
||||
llm = chatModel.model;
|
||||
}
|
||||
@ -6,7 +6,7 @@ import {
|
||||
} from '@/lib/config';
|
||||
import { getAvailableChatModelProviders } from '@/lib/providers';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { AIMessage, HumanMessage } from '@langchain/core/messages';
|
||||
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
|
||||
interface ChatModel {
|
||||
@ -31,7 +31,7 @@ export const POST = async (req: Request) => {
|
||||
return new AIMessage(msg.content);
|
||||
}
|
||||
})
|
||||
.filter((msg) => msg !== undefined);
|
||||
.filter((msg) => msg !== undefined) as BaseMessage[];
|
||||
|
||||
const chatModelProviders = await getAvailableChatModelProviders();
|
||||
|
||||
@ -54,7 +54,7 @@ export const POST = async (req: Request) => {
|
||||
configuration: {
|
||||
baseURL: getCustomOpenaiApiUrl(),
|
||||
},
|
||||
});
|
||||
}) as unknown as BaseChatModel;
|
||||
} else if (chatModelProvider && chatModel) {
|
||||
llm = chatModel.model;
|
||||
}
|
||||
@ -6,7 +6,7 @@ import {
|
||||
} from '@/lib/config';
|
||||
import { getAvailableChatModelProviders } from '@/lib/providers';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { AIMessage, HumanMessage } from '@langchain/core/messages';
|
||||
import { AIMessage, BaseMessage, HumanMessage } from '@langchain/core/messages';
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
|
||||
interface ChatModel {
|
||||
@ -32,7 +32,7 @@ export const POST = async (req: Request) => {
|
||||
return new AIMessage(msg.content);
|
||||
}
|
||||
})
|
||||
.filter((msg) => msg !== undefined);
|
||||
.filter((msg) => msg !== undefined) as BaseMessage[];
|
||||
|
||||
const chatModelProviders = await getAvailableChatModelProviders();
|
||||
|
||||
@ -55,7 +55,7 @@ export const POST = async (req: Request) => {
|
||||
configuration: {
|
||||
baseURL: getCustomOpenaiApiUrl(),
|
||||
},
|
||||
});
|
||||
}) as unknown as BaseChatModel;
|
||||
} else if (chatModelProvider && chatModel) {
|
||||
llm = chatModel.model;
|
||||
}
|
||||
|
Before Width: | Height: | Size: 25 KiB After Width: | Height: | Size: 25 KiB |
@ -1,84 +0,0 @@
|
||||
import {
|
||||
RunnableSequence,
|
||||
RunnableMap,
|
||||
RunnableLambda,
|
||||
} from '@langchain/core/runnables';
|
||||
import { PromptTemplate } from '@langchain/core/prompts';
|
||||
import formatChatHistoryAsString from '../utils/formatHistory';
|
||||
import { BaseMessage } from '@langchain/core/messages';
|
||||
import { StringOutputParser } from '@langchain/core/output_parsers';
|
||||
import { searchSearxng } from '../lib/searxng';
|
||||
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
|
||||
const imageSearchChainPrompt = `
|
||||
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search the web for images.
|
||||
You need to make sure the rephrased question agrees with the conversation and is relevant to the conversation.
|
||||
|
||||
Example:
|
||||
1. Follow up question: What is a cat?
|
||||
Rephrased: A cat
|
||||
|
||||
2. Follow up question: What is a car? How does it works?
|
||||
Rephrased: Car working
|
||||
|
||||
3. Follow up question: How does an AC work?
|
||||
Rephrased: AC working
|
||||
|
||||
Conversation:
|
||||
{chat_history}
|
||||
|
||||
Follow up question: {query}
|
||||
Rephrased question:
|
||||
`;
|
||||
|
||||
type ImageSearchChainInput = {
|
||||
chat_history: BaseMessage[];
|
||||
query: string;
|
||||
};
|
||||
|
||||
const strParser = new StringOutputParser();
|
||||
|
||||
const createImageSearchChain = (llm: BaseChatModel) => {
|
||||
return RunnableSequence.from([
|
||||
RunnableMap.from({
|
||||
chat_history: (input: ImageSearchChainInput) => {
|
||||
return formatChatHistoryAsString(input.chat_history);
|
||||
},
|
||||
query: (input: ImageSearchChainInput) => {
|
||||
return input.query;
|
||||
},
|
||||
}),
|
||||
PromptTemplate.fromTemplate(imageSearchChainPrompt),
|
||||
llm,
|
||||
strParser,
|
||||
RunnableLambda.from(async (input: string) => {
|
||||
const res = await searchSearxng(input, {
|
||||
engines: ['bing images', 'google images'],
|
||||
});
|
||||
|
||||
const images = [];
|
||||
|
||||
res.results.forEach((result) => {
|
||||
if (result.img_src && result.url && result.title) {
|
||||
images.push({
|
||||
img_src: result.img_src,
|
||||
url: result.url,
|
||||
title: result.title,
|
||||
});
|
||||
}
|
||||
});
|
||||
|
||||
return images.slice(0, 10);
|
||||
}),
|
||||
]);
|
||||
};
|
||||
|
||||
const handleImageSearch = (
|
||||
input: ImageSearchChainInput,
|
||||
llm: BaseChatModel,
|
||||
) => {
|
||||
const imageSearchChain = createImageSearchChain(llm);
|
||||
return imageSearchChain.invoke(input);
|
||||
};
|
||||
|
||||
export default handleImageSearch;
|
||||
@ -1,55 +0,0 @@
|
||||
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>
|
||||
Tell me more about SpaceX and their recent projects
|
||||
What is the latest news on SpaceX?
|
||||
Who is the CEO of SpaceX?
|
||||
</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 unknown as ChatOpenAI).temperature = 0;
|
||||
const suggestionGeneratorChain = createSuggestionGeneratorChain(llm);
|
||||
return suggestionGeneratorChain.invoke(input);
|
||||
};
|
||||
|
||||
export default generateSuggestions;
|
||||
@ -1,90 +0,0 @@
|
||||
import {
|
||||
RunnableSequence,
|
||||
RunnableMap,
|
||||
RunnableLambda,
|
||||
} from '@langchain/core/runnables';
|
||||
import { PromptTemplate } from '@langchain/core/prompts';
|
||||
import formatChatHistoryAsString from '../utils/formatHistory';
|
||||
import { BaseMessage } from '@langchain/core/messages';
|
||||
import { StringOutputParser } from '@langchain/core/output_parsers';
|
||||
import { searchSearxng } from '../lib/searxng';
|
||||
import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
|
||||
const VideoSearchChainPrompt = `
|
||||
You will be given a conversation below and a follow up question. You need to rephrase the follow-up question so it is a standalone question that can be used by the LLM to search Youtube for videos.
|
||||
You need to make sure the rephrased question agrees with the conversation and is relevant to the conversation.
|
||||
|
||||
Example:
|
||||
1. Follow up question: How does a car work?
|
||||
Rephrased: How does a car work?
|
||||
|
||||
2. Follow up question: What is the theory of relativity?
|
||||
Rephrased: What is theory of relativity
|
||||
|
||||
3. Follow up question: How does an AC work?
|
||||
Rephrased: How does an AC work
|
||||
|
||||
Conversation:
|
||||
{chat_history}
|
||||
|
||||
Follow up question: {query}
|
||||
Rephrased question:
|
||||
`;
|
||||
|
||||
type VideoSearchChainInput = {
|
||||
chat_history: BaseMessage[];
|
||||
query: string;
|
||||
};
|
||||
|
||||
const strParser = new StringOutputParser();
|
||||
|
||||
const createVideoSearchChain = (llm: BaseChatModel) => {
|
||||
return RunnableSequence.from([
|
||||
RunnableMap.from({
|
||||
chat_history: (input: VideoSearchChainInput) => {
|
||||
return formatChatHistoryAsString(input.chat_history);
|
||||
},
|
||||
query: (input: VideoSearchChainInput) => {
|
||||
return input.query;
|
||||
},
|
||||
}),
|
||||
PromptTemplate.fromTemplate(VideoSearchChainPrompt),
|
||||
llm,
|
||||
strParser,
|
||||
RunnableLambda.from(async (input: string) => {
|
||||
const res = await searchSearxng(input, {
|
||||
engines: ['youtube'],
|
||||
});
|
||||
|
||||
const videos = [];
|
||||
|
||||
res.results.forEach((result) => {
|
||||
if (
|
||||
result.thumbnail &&
|
||||
result.url &&
|
||||
result.title &&
|
||||
result.iframe_src
|
||||
) {
|
||||
videos.push({
|
||||
img_src: result.thumbnail,
|
||||
url: result.url,
|
||||
title: result.title,
|
||||
iframe_src: result.iframe_src,
|
||||
});
|
||||
}
|
||||
});
|
||||
|
||||
return videos.slice(0, 10);
|
||||
}),
|
||||
]);
|
||||
};
|
||||
|
||||
const handleVideoSearch = (
|
||||
input: VideoSearchChainInput,
|
||||
llm: BaseChatModel,
|
||||
) => {
|
||||
const VideoSearchChain = createVideoSearchChain(llm);
|
||||
return VideoSearchChain.invoke(input);
|
||||
};
|
||||
|
||||
export default handleVideoSearch;
|
||||
@ -45,159 +45,138 @@ const checkConfig = async (
|
||||
setIsConfigReady: (ready: boolean) => void,
|
||||
setHasError: (hasError: boolean) => void,
|
||||
) => {
|
||||
useEffect(() => {
|
||||
const checkConfig = async () => {
|
||||
try {
|
||||
let chatModel = localStorage.getItem('chatModel');
|
||||
let chatModelProvider = localStorage.getItem('chatModelProvider');
|
||||
let embeddingModel = localStorage.getItem('embeddingModel');
|
||||
let embeddingModelProvider = localStorage.getItem(
|
||||
'embeddingModelProvider',
|
||||
try {
|
||||
let chatModel = localStorage.getItem('chatModel');
|
||||
let chatModelProvider = localStorage.getItem('chatModelProvider');
|
||||
let embeddingModel = localStorage.getItem('embeddingModel');
|
||||
let embeddingModelProvider = localStorage.getItem('embeddingModelProvider');
|
||||
|
||||
const autoImageSearch = localStorage.getItem('autoImageSearch');
|
||||
const autoVideoSearch = localStorage.getItem('autoVideoSearch');
|
||||
|
||||
if (!autoImageSearch) {
|
||||
localStorage.setItem('autoImageSearch', 'true');
|
||||
}
|
||||
|
||||
if (!autoVideoSearch) {
|
||||
localStorage.setItem('autoVideoSearch', 'false');
|
||||
}
|
||||
|
||||
const providers = await fetch(`/api/models`, {
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
}).then(async (res) => {
|
||||
if (!res.ok)
|
||||
throw new Error(
|
||||
`Failed to fetch models: ${res.status} ${res.statusText}`,
|
||||
);
|
||||
return res.json();
|
||||
});
|
||||
|
||||
const autoImageSearch = localStorage.getItem('autoImageSearch');
|
||||
const autoVideoSearch = localStorage.getItem('autoVideoSearch');
|
||||
if (
|
||||
!chatModel ||
|
||||
!chatModelProvider ||
|
||||
!embeddingModel ||
|
||||
!embeddingModelProvider
|
||||
) {
|
||||
if (!chatModel || !chatModelProvider) {
|
||||
const chatModelProviders = providers.chatModelProviders;
|
||||
|
||||
if (!autoImageSearch) {
|
||||
localStorage.setItem('autoImageSearch', 'true');
|
||||
}
|
||||
chatModelProvider =
|
||||
chatModelProvider || Object.keys(chatModelProviders)[0];
|
||||
|
||||
if (!autoVideoSearch) {
|
||||
localStorage.setItem('autoVideoSearch', 'false');
|
||||
}
|
||||
chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
|
||||
|
||||
const providers = await fetch(`/api/models`, {
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
}).then(async (res) => {
|
||||
if (!res.ok)
|
||||
throw new Error(
|
||||
`Failed to fetch models: ${res.status} ${res.statusText}`,
|
||||
);
|
||||
return res.json();
|
||||
});
|
||||
if (!chatModelProviders || Object.keys(chatModelProviders).length === 0)
|
||||
return toast.error('No chat models available');
|
||||
}
|
||||
|
||||
if (!embeddingModel || !embeddingModelProvider) {
|
||||
const embeddingModelProviders = providers.embeddingModelProviders;
|
||||
|
||||
if (
|
||||
!chatModel ||
|
||||
!chatModelProvider ||
|
||||
!embeddingModel ||
|
||||
!embeddingModelProvider
|
||||
) {
|
||||
if (!chatModel || !chatModelProvider) {
|
||||
const chatModelProviders = providers.chatModelProviders;
|
||||
!embeddingModelProviders ||
|
||||
Object.keys(embeddingModelProviders).length === 0
|
||||
)
|
||||
return toast.error('No embedding models available');
|
||||
|
||||
chatModelProvider =
|
||||
chatModelProvider || Object.keys(chatModelProviders)[0];
|
||||
|
||||
chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
|
||||
|
||||
if (
|
||||
!chatModelProviders ||
|
||||
Object.keys(chatModelProviders).length === 0
|
||||
)
|
||||
return toast.error('No chat models available');
|
||||
}
|
||||
|
||||
if (!embeddingModel || !embeddingModelProvider) {
|
||||
const embeddingModelProviders = providers.embeddingModelProviders;
|
||||
|
||||
if (
|
||||
!embeddingModelProviders ||
|
||||
Object.keys(embeddingModelProviders).length === 0
|
||||
)
|
||||
return toast.error('No embedding models available');
|
||||
|
||||
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
|
||||
embeddingModel = Object.keys(
|
||||
embeddingModelProviders[embeddingModelProvider],
|
||||
)[0];
|
||||
}
|
||||
|
||||
localStorage.setItem('chatModel', chatModel!);
|
||||
localStorage.setItem('chatModelProvider', chatModelProvider);
|
||||
localStorage.setItem('embeddingModel', embeddingModel!);
|
||||
localStorage.setItem(
|
||||
'embeddingModelProvider',
|
||||
embeddingModelProvider,
|
||||
);
|
||||
} else {
|
||||
const chatModelProviders = providers.chatModelProviders;
|
||||
const embeddingModelProviders = providers.embeddingModelProviders;
|
||||
|
||||
if (
|
||||
Object.keys(chatModelProviders).length > 0 &&
|
||||
!chatModelProviders[chatModelProvider]
|
||||
) {
|
||||
const chatModelProvidersKeys = Object.keys(chatModelProviders);
|
||||
chatModelProvider =
|
||||
chatModelProvidersKeys.find(
|
||||
(key) => Object.keys(chatModelProviders[key]).length > 0,
|
||||
) || chatModelProvidersKeys[0];
|
||||
|
||||
localStorage.setItem('chatModelProvider', chatModelProvider);
|
||||
}
|
||||
|
||||
if (
|
||||
chatModelProvider &&
|
||||
!chatModelProviders[chatModelProvider][chatModel]
|
||||
) {
|
||||
chatModel = Object.keys(
|
||||
chatModelProviders[
|
||||
Object.keys(chatModelProviders[chatModelProvider]).length > 0
|
||||
? chatModelProvider
|
||||
: Object.keys(chatModelProviders)[0]
|
||||
],
|
||||
)[0];
|
||||
localStorage.setItem('chatModel', chatModel);
|
||||
}
|
||||
|
||||
if (
|
||||
Object.keys(embeddingModelProviders).length > 0 &&
|
||||
!embeddingModelProviders[embeddingModelProvider]
|
||||
) {
|
||||
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
|
||||
localStorage.setItem(
|
||||
'embeddingModelProvider',
|
||||
embeddingModelProvider,
|
||||
);
|
||||
}
|
||||
|
||||
if (
|
||||
embeddingModelProvider &&
|
||||
!embeddingModelProviders[embeddingModelProvider][embeddingModel]
|
||||
) {
|
||||
embeddingModel = Object.keys(
|
||||
embeddingModelProviders[embeddingModelProvider],
|
||||
)[0];
|
||||
localStorage.setItem('embeddingModel', embeddingModel);
|
||||
}
|
||||
}
|
||||
|
||||
setChatModelProvider({
|
||||
name: chatModel!,
|
||||
provider: chatModelProvider,
|
||||
});
|
||||
|
||||
setEmbeddingModelProvider({
|
||||
name: embeddingModel!,
|
||||
provider: embeddingModelProvider,
|
||||
});
|
||||
|
||||
setIsConfigReady(true);
|
||||
} catch (err) {
|
||||
console.error(
|
||||
'An error occurred while checking the configuration:',
|
||||
err,
|
||||
);
|
||||
setIsConfigReady(false);
|
||||
setHasError(true);
|
||||
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
|
||||
embeddingModel = Object.keys(
|
||||
embeddingModelProviders[embeddingModelProvider],
|
||||
)[0];
|
||||
}
|
||||
};
|
||||
|
||||
checkConfig();
|
||||
// eslint-disable-next-line react-hooks/exhaustive-deps
|
||||
}, []);
|
||||
localStorage.setItem('chatModel', chatModel!);
|
||||
localStorage.setItem('chatModelProvider', chatModelProvider);
|
||||
localStorage.setItem('embeddingModel', embeddingModel!);
|
||||
localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
|
||||
} else {
|
||||
const chatModelProviders = providers.chatModelProviders;
|
||||
const embeddingModelProviders = providers.embeddingModelProviders;
|
||||
|
||||
if (
|
||||
Object.keys(chatModelProviders).length > 0 &&
|
||||
!chatModelProviders[chatModelProvider]
|
||||
) {
|
||||
const chatModelProvidersKeys = Object.keys(chatModelProviders);
|
||||
chatModelProvider =
|
||||
chatModelProvidersKeys.find(
|
||||
(key) => Object.keys(chatModelProviders[key]).length > 0,
|
||||
) || chatModelProvidersKeys[0];
|
||||
|
||||
localStorage.setItem('chatModelProvider', chatModelProvider);
|
||||
}
|
||||
|
||||
if (
|
||||
chatModelProvider &&
|
||||
!chatModelProviders[chatModelProvider][chatModel]
|
||||
) {
|
||||
chatModel = Object.keys(
|
||||
chatModelProviders[
|
||||
Object.keys(chatModelProviders[chatModelProvider]).length > 0
|
||||
? chatModelProvider
|
||||
: Object.keys(chatModelProviders)[0]
|
||||
],
|
||||
)[0];
|
||||
localStorage.setItem('chatModel', chatModel);
|
||||
}
|
||||
|
||||
if (
|
||||
Object.keys(embeddingModelProviders).length > 0 &&
|
||||
!embeddingModelProviders[embeddingModelProvider]
|
||||
) {
|
||||
embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
|
||||
localStorage.setItem('embeddingModelProvider', embeddingModelProvider);
|
||||
}
|
||||
|
||||
if (
|
||||
embeddingModelProvider &&
|
||||
!embeddingModelProviders[embeddingModelProvider][embeddingModel]
|
||||
) {
|
||||
embeddingModel = Object.keys(
|
||||
embeddingModelProviders[embeddingModelProvider],
|
||||
)[0];
|
||||
localStorage.setItem('embeddingModel', embeddingModel);
|
||||
}
|
||||
}
|
||||
|
||||
setChatModelProvider({
|
||||
name: chatModel!,
|
||||
provider: chatModelProvider,
|
||||
});
|
||||
|
||||
setEmbeddingModelProvider({
|
||||
name: embeddingModel!,
|
||||
provider: embeddingModelProvider,
|
||||
});
|
||||
|
||||
setIsConfigReady(true);
|
||||
} catch (err) {
|
||||
console.error('An error occurred while checking the configuration:', err);
|
||||
setIsConfigReady(false);
|
||||
setHasError(true);
|
||||
}
|
||||
};
|
||||
|
||||
const loadMessages = async (
|
||||
@ -282,12 +261,15 @@ const ChatWindow = ({ id }: { id?: string }) => {
|
||||
const [hasError, setHasError] = useState(false);
|
||||
const [isReady, setIsReady] = useState(false);
|
||||
|
||||
checkConfig(
|
||||
setChatModelProvider,
|
||||
setEmbeddingModelProvider,
|
||||
setIsConfigReady,
|
||||
setHasError,
|
||||
);
|
||||
useEffect(() => {
|
||||
checkConfig(
|
||||
setChatModelProvider,
|
||||
setEmbeddingModelProvider,
|
||||
setIsConfigReady,
|
||||
setHasError,
|
||||
);
|
||||
// eslint-disable-next-line react-hooks/exhaustive-deps
|
||||
}, []);
|
||||
|
||||
const [loading, setLoading] = useState(false);
|
||||
const [messageAppeared, setMessageAppeared] = useState(false);
|
||||
117
src/config.ts
117
src/config.ts
@ -1,117 +0,0 @@
|
||||
import fs from 'fs';
|
||||
import path from 'path';
|
||||
import toml from '@iarna/toml';
|
||||
|
||||
const configFileName = 'config.toml';
|
||||
|
||||
interface Config {
|
||||
GENERAL: {
|
||||
PORT: number;
|
||||
SIMILARITY_MEASURE: string;
|
||||
KEEP_ALIVE: string;
|
||||
};
|
||||
MODELS: {
|
||||
OPENAI: {
|
||||
API_KEY: string;
|
||||
};
|
||||
GROQ: {
|
||||
API_KEY: string;
|
||||
};
|
||||
ANTHROPIC: {
|
||||
API_KEY: string;
|
||||
};
|
||||
GEMINI: {
|
||||
API_KEY: string;
|
||||
};
|
||||
OLLAMA: {
|
||||
API_URL: string;
|
||||
};
|
||||
CUSTOM_OPENAI: {
|
||||
API_URL: string;
|
||||
API_KEY: string;
|
||||
MODEL_NAME: string;
|
||||
};
|
||||
};
|
||||
API_ENDPOINTS: {
|
||||
SEARXNG: string;
|
||||
};
|
||||
}
|
||||
|
||||
type RecursivePartial<T> = {
|
||||
[P in keyof T]?: RecursivePartial<T[P]>;
|
||||
};
|
||||
|
||||
const loadConfig = () =>
|
||||
toml.parse(
|
||||
fs.readFileSync(path.join(__dirname, `../${configFileName}`), 'utf-8'),
|
||||
) as any as Config;
|
||||
|
||||
export const getPort = () => loadConfig().GENERAL.PORT;
|
||||
|
||||
export const getSimilarityMeasure = () =>
|
||||
loadConfig().GENERAL.SIMILARITY_MEASURE;
|
||||
|
||||
export const getKeepAlive = () => loadConfig().GENERAL.KEEP_ALIVE;
|
||||
|
||||
export const getOpenaiApiKey = () => loadConfig().MODELS.OPENAI.API_KEY;
|
||||
|
||||
export const getGroqApiKey = () => loadConfig().MODELS.GROQ.API_KEY;
|
||||
|
||||
export const getAnthropicApiKey = () => loadConfig().MODELS.ANTHROPIC.API_KEY;
|
||||
|
||||
export const getGeminiApiKey = () => loadConfig().MODELS.GEMINI.API_KEY;
|
||||
|
||||
export const getSearxngApiEndpoint = () =>
|
||||
process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
|
||||
|
||||
export const getOllamaApiEndpoint = () => loadConfig().MODELS.OLLAMA.API_URL;
|
||||
|
||||
export const getCustomOpenaiApiKey = () =>
|
||||
loadConfig().MODELS.CUSTOM_OPENAI.API_KEY;
|
||||
|
||||
export const getCustomOpenaiApiUrl = () =>
|
||||
loadConfig().MODELS.CUSTOM_OPENAI.API_URL;
|
||||
|
||||
export const getCustomOpenaiModelName = () =>
|
||||
loadConfig().MODELS.CUSTOM_OPENAI.MODEL_NAME;
|
||||
|
||||
const mergeConfigs = (current: any, update: any): any => {
|
||||
if (update === null || update === undefined) {
|
||||
return current;
|
||||
}
|
||||
|
||||
if (typeof current !== 'object' || current === null) {
|
||||
return update;
|
||||
}
|
||||
|
||||
const result = { ...current };
|
||||
|
||||
for (const key in update) {
|
||||
if (Object.prototype.hasOwnProperty.call(update, key)) {
|
||||
const updateValue = update[key];
|
||||
|
||||
if (
|
||||
typeof updateValue === 'object' &&
|
||||
updateValue !== null &&
|
||||
typeof result[key] === 'object' &&
|
||||
result[key] !== null
|
||||
) {
|
||||
result[key] = mergeConfigs(result[key], updateValue);
|
||||
} else if (updateValue !== undefined) {
|
||||
result[key] = updateValue;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return result;
|
||||
};
|
||||
|
||||
export const updateConfig = (config: RecursivePartial<Config>) => {
|
||||
const currentConfig = loadConfig();
|
||||
const mergedConfig = mergeConfigs(currentConfig, config);
|
||||
|
||||
fs.writeFileSync(
|
||||
path.join(__dirname, `../${configFileName}`),
|
||||
toml.stringify(mergedConfig),
|
||||
);
|
||||
};
|
||||
@ -1,10 +0,0 @@
|
||||
import { drizzle } from 'drizzle-orm/better-sqlite3';
|
||||
import Database from 'better-sqlite3';
|
||||
import * as schema from './schema';
|
||||
|
||||
const sqlite = new Database('data/db.sqlite');
|
||||
const db = drizzle(sqlite, {
|
||||
schema: schema,
|
||||
});
|
||||
|
||||
export default db;
|
||||
@ -62,7 +62,7 @@ export const getAnthropicApiKey = () => loadConfig().MODELS.ANTHROPIC.API_KEY;
|
||||
export const getGeminiApiKey = () => loadConfig().MODELS.GEMINI.API_KEY;
|
||||
|
||||
export const getSearxngApiEndpoint = () =>
|
||||
process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
|
||||
loadConfig().API_ENDPOINTS.SEARXNG || process.env.SEARXNG_API_URL;
|
||||
|
||||
export const getOllamaApiEndpoint = () => loadConfig().MODELS.OLLAMA.API_URL;
|
||||
|
||||
@ -1,82 +0,0 @@
|
||||
import { Embeddings, type EmbeddingsParams } from '@langchain/core/embeddings';
|
||||
import { chunkArray } from '@langchain/core/utils/chunk_array';
|
||||
|
||||
export interface HuggingFaceTransformersEmbeddingsParams
|
||||
extends EmbeddingsParams {
|
||||
modelName: string;
|
||||
|
||||
model: string;
|
||||
|
||||
timeout?: number;
|
||||
|
||||
batchSize?: number;
|
||||
|
||||
stripNewLines?: boolean;
|
||||
}
|
||||
|
||||
export class HuggingFaceTransformersEmbeddings
|
||||
extends Embeddings
|
||||
implements HuggingFaceTransformersEmbeddingsParams
|
||||
{
|
||||
modelName = 'Xenova/all-MiniLM-L6-v2';
|
||||
|
||||
model = 'Xenova/all-MiniLM-L6-v2';
|
||||
|
||||
batchSize = 512;
|
||||
|
||||
stripNewLines = true;
|
||||
|
||||
timeout?: number;
|
||||
|
||||
private pipelinePromise: Promise<any>;
|
||||
|
||||
constructor(fields?: Partial<HuggingFaceTransformersEmbeddingsParams>) {
|
||||
super(fields ?? {});
|
||||
|
||||
this.modelName = fields?.model ?? fields?.modelName ?? this.model;
|
||||
this.model = this.modelName;
|
||||
this.stripNewLines = fields?.stripNewLines ?? this.stripNewLines;
|
||||
this.timeout = fields?.timeout;
|
||||
}
|
||||
|
||||
async embedDocuments(texts: string[]): Promise<number[][]> {
|
||||
const batches = chunkArray(
|
||||
this.stripNewLines ? texts.map((t) => t.replace(/\n/g, ' ')) : texts,
|
||||
this.batchSize,
|
||||
);
|
||||
|
||||
const batchRequests = batches.map((batch) => this.runEmbedding(batch));
|
||||
const batchResponses = await Promise.all(batchRequests);
|
||||
const embeddings: number[][] = [];
|
||||
|
||||
for (let i = 0; i < batchResponses.length; i += 1) {
|
||||
const batchResponse = batchResponses[i];
|
||||
for (let j = 0; j < batchResponse.length; j += 1) {
|
||||
embeddings.push(batchResponse[j]);
|
||||
}
|
||||
}
|
||||
|
||||
return embeddings;
|
||||
}
|
||||
|
||||
async embedQuery(text: string): Promise<number[]> {
|
||||
const data = await this.runEmbedding([
|
||||
this.stripNewLines ? text.replace(/\n/g, ' ') : text,
|
||||
]);
|
||||
return data[0];
|
||||
}
|
||||
|
||||
private async runEmbedding(texts: string[]) {
|
||||
const { pipeline } = await import('@xenova/transformers');
|
||||
|
||||
const pipe = await (this.pipelinePromise ??= pipeline(
|
||||
'feature-extraction',
|
||||
this.model,
|
||||
));
|
||||
|
||||
return this.caller.call(async () => {
|
||||
const output = await pipe(texts, { pooling: 'mean', normalize: true });
|
||||
return output.tolist();
|
||||
});
|
||||
}
|
||||
}
|
||||
@ -9,7 +9,7 @@ class LineOutputParser extends BaseOutputParser<string> {
|
||||
|
||||
constructor(args?: LineOutputParserArgs) {
|
||||
super();
|
||||
this.key = args.key ?? this.key;
|
||||
this.key = args?.key ?? this.key;
|
||||
}
|
||||
|
||||
static lc_name() {
|
||||
|
||||
@ -9,7 +9,7 @@ class LineListOutputParser extends BaseOutputParser<string[]> {
|
||||
|
||||
constructor(args?: LineListOutputParserArgs) {
|
||||
super();
|
||||
this.key = args.key ?? this.key;
|
||||
this.key = args?.key ?? this.key;
|
||||
}
|
||||
|
||||
static lc_name() {
|
||||
|
||||
@ -1,6 +1,38 @@
|
||||
import { ChatAnthropic } from '@langchain/anthropic';
|
||||
import { getAnthropicApiKey } from '../../config';
|
||||
import logger from '../../utils/logger';
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
import { ChatModel } from '.';
|
||||
import { getAnthropicApiKey } from '../config';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
|
||||
const anthropicChatModels: Record<string, string>[] = [
|
||||
{
|
||||
displayName: 'Claude 3.7 Sonnet',
|
||||
key: 'claude-3-7-sonnet-20250219',
|
||||
},
|
||||
{
|
||||
displayName: 'Claude 3.5 Haiku',
|
||||
key: 'claude-3-5-haiku-20241022',
|
||||
},
|
||||
{
|
||||
displayName: 'Claude 3.5 Sonnet v2',
|
||||
key: 'claude-3-5-sonnet-20241022',
|
||||
},
|
||||
{
|
||||
displayName: 'Claude 3.5 Sonnet',
|
||||
key: 'claude-3-5-sonnet-20240620',
|
||||
},
|
||||
{
|
||||
displayName: 'Claude 3 Opus',
|
||||
key: 'claude-3-opus-20240229',
|
||||
},
|
||||
{
|
||||
displayName: 'Claude 3 Sonnet',
|
||||
key: 'claude-3-sonnet-20240229',
|
||||
},
|
||||
{
|
||||
displayName: 'Claude 3 Haiku',
|
||||
key: 'claude-3-haiku-20240307',
|
||||
},
|
||||
];
|
||||
|
||||
export const loadAnthropicChatModels = async () => {
|
||||
const anthropicApiKey = getAnthropicApiKey();
|
||||
@ -8,52 +40,25 @@ export const loadAnthropicChatModels = async () => {
|
||||
if (!anthropicApiKey) return {};
|
||||
|
||||
try {
|
||||
const chatModels = {
|
||||
'claude-3-5-sonnet-20241022': {
|
||||
displayName: 'Claude 3.5 Sonnet',
|
||||
model: new ChatAnthropic({
|
||||
const chatModels: Record<string, ChatModel> = {};
|
||||
|
||||
anthropicChatModels.forEach((model) => {
|
||||
chatModels[model.key] = {
|
||||
displayName: model.displayName,
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey: anthropicApiKey,
|
||||
modelName: model.key,
|
||||
temperature: 0.7,
|
||||
anthropicApiKey: anthropicApiKey,
|
||||
model: 'claude-3-5-sonnet-20241022',
|
||||
}),
|
||||
},
|
||||
'claude-3-5-haiku-20241022': {
|
||||
displayName: 'Claude 3.5 Haiku',
|
||||
model: new ChatAnthropic({
|
||||
temperature: 0.7,
|
||||
anthropicApiKey: anthropicApiKey,
|
||||
model: 'claude-3-5-haiku-20241022',
|
||||
}),
|
||||
},
|
||||
'claude-3-opus-20240229': {
|
||||
displayName: 'Claude 3 Opus',
|
||||
model: new ChatAnthropic({
|
||||
temperature: 0.7,
|
||||
anthropicApiKey: anthropicApiKey,
|
||||
model: 'claude-3-opus-20240229',
|
||||
}),
|
||||
},
|
||||
'claude-3-sonnet-20240229': {
|
||||
displayName: 'Claude 3 Sonnet',
|
||||
model: new ChatAnthropic({
|
||||
temperature: 0.7,
|
||||
anthropicApiKey: anthropicApiKey,
|
||||
model: 'claude-3-sonnet-20240229',
|
||||
}),
|
||||
},
|
||||
'claude-3-haiku-20240307': {
|
||||
displayName: 'Claude 3 Haiku',
|
||||
model: new ChatAnthropic({
|
||||
temperature: 0.7,
|
||||
anthropicApiKey: anthropicApiKey,
|
||||
model: 'claude-3-haiku-20240307',
|
||||
}),
|
||||
},
|
||||
};
|
||||
configuration: {
|
||||
baseURL: 'https://api.anthropic.com/v1/',
|
||||
},
|
||||
}) as unknown as BaseChatModel,
|
||||
};
|
||||
});
|
||||
|
||||
return chatModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Anthropic models: ${err}`);
|
||||
console.error(`Error loading Anthropic models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
||||
@ -1,9 +1,42 @@
|
||||
import {
|
||||
ChatGoogleGenerativeAI,
|
||||
GoogleGenerativeAIEmbeddings,
|
||||
} from '@langchain/google-genai';
|
||||
import { getGeminiApiKey } from '../../config';
|
||||
import logger from '../../utils/logger';
|
||||
import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
|
||||
import { getGeminiApiKey } from '../config';
|
||||
import { ChatModel, EmbeddingModel } from '.';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { Embeddings } from '@langchain/core/embeddings';
|
||||
|
||||
const geminiChatModels: Record<string, string>[] = [
|
||||
{
|
||||
displayName: 'Gemini 2.0 Flash',
|
||||
key: 'gemini-2.0-flash',
|
||||
},
|
||||
{
|
||||
displayName: 'Gemini 2.0 Flash-Lite',
|
||||
key: 'gemini-2.0-flash-lite',
|
||||
},
|
||||
{
|
||||
displayName: 'Gemini 2.0 Pro Experimental',
|
||||
key: 'gemini-2.0-pro-exp-02-05',
|
||||
},
|
||||
{
|
||||
displayName: 'Gemini 1.5 Flash',
|
||||
key: 'gemini-1.5-flash',
|
||||
},
|
||||
{
|
||||
displayName: 'Gemini 1.5 Flash-8B',
|
||||
key: 'gemini-1.5-flash-8b',
|
||||
},
|
||||
{
|
||||
displayName: 'Gemini 1.5 Pro',
|
||||
key: 'gemini-1.5-pro',
|
||||
},
|
||||
];
|
||||
|
||||
const geminiEmbeddingModels: Record<string, string>[] = [
|
||||
{
|
||||
displayName: 'Gemini Embedding',
|
||||
key: 'gemini-embedding-exp',
|
||||
},
|
||||
];
|
||||
|
||||
export const loadGeminiChatModels = async () => {
|
||||
const geminiApiKey = getGeminiApiKey();
|
||||
@ -11,75 +44,53 @@ export const loadGeminiChatModels = async () => {
|
||||
if (!geminiApiKey) return {};
|
||||
|
||||
try {
|
||||
const chatModels = {
|
||||
'gemini-1.5-flash': {
|
||||
displayName: 'Gemini 1.5 Flash',
|
||||
model: new ChatGoogleGenerativeAI({
|
||||
modelName: 'gemini-1.5-flash',
|
||||
const chatModels: Record<string, ChatModel> = {};
|
||||
|
||||
geminiChatModels.forEach((model) => {
|
||||
chatModels[model.key] = {
|
||||
displayName: model.displayName,
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey: geminiApiKey,
|
||||
modelName: model.key,
|
||||
temperature: 0.7,
|
||||
apiKey: geminiApiKey,
|
||||
}),
|
||||
},
|
||||
'gemini-1.5-flash-8b': {
|
||||
displayName: 'Gemini 1.5 Flash 8B',
|
||||
model: new ChatGoogleGenerativeAI({
|
||||
modelName: 'gemini-1.5-flash-8b',
|
||||
temperature: 0.7,
|
||||
apiKey: geminiApiKey,
|
||||
}),
|
||||
},
|
||||
'gemini-1.5-pro': {
|
||||
displayName: 'Gemini 1.5 Pro',
|
||||
model: new ChatGoogleGenerativeAI({
|
||||
modelName: 'gemini-1.5-pro',
|
||||
temperature: 0.7,
|
||||
apiKey: geminiApiKey,
|
||||
}),
|
||||
},
|
||||
'gemini-2.0-flash-exp': {
|
||||
displayName: 'Gemini 2.0 Flash Exp',
|
||||
model: new ChatGoogleGenerativeAI({
|
||||
modelName: 'gemini-2.0-flash-exp',
|
||||
temperature: 0.7,
|
||||
apiKey: geminiApiKey,
|
||||
}),
|
||||
},
|
||||
'gemini-2.0-flash-thinking-exp-01-21': {
|
||||
displayName: 'Gemini 2.0 Flash Thinking Exp 01-21',
|
||||
model: new ChatGoogleGenerativeAI({
|
||||
modelName: 'gemini-2.0-flash-thinking-exp-01-21',
|
||||
temperature: 0.7,
|
||||
apiKey: geminiApiKey,
|
||||
}),
|
||||
},
|
||||
};
|
||||
configuration: {
|
||||
baseURL: 'https://generativelanguage.googleapis.com/v1beta/openai/',
|
||||
},
|
||||
}) as unknown as BaseChatModel,
|
||||
};
|
||||
});
|
||||
|
||||
return chatModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Gemini models: ${err}`);
|
||||
console.error(`Error loading Gemini models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
||||
export const loadGeminiEmbeddingsModels = async () => {
|
||||
export const loadGeminiEmbeddingModels = async () => {
|
||||
const geminiApiKey = getGeminiApiKey();
|
||||
|
||||
if (!geminiApiKey) return {};
|
||||
|
||||
try {
|
||||
const embeddingModels = {
|
||||
'text-embedding-004': {
|
||||
displayName: 'Text Embedding',
|
||||
model: new GoogleGenerativeAIEmbeddings({
|
||||
apiKey: geminiApiKey,
|
||||
modelName: 'text-embedding-004',
|
||||
}),
|
||||
},
|
||||
};
|
||||
const embeddingModels: Record<string, EmbeddingModel> = {};
|
||||
|
||||
geminiEmbeddingModels.forEach((model) => {
|
||||
embeddingModels[model.key] = {
|
||||
displayName: model.displayName,
|
||||
model: new OpenAIEmbeddings({
|
||||
openAIApiKey: geminiApiKey,
|
||||
modelName: model.key,
|
||||
configuration: {
|
||||
baseURL: 'https://generativelanguage.googleapis.com/v1beta/openai/',
|
||||
},
|
||||
}) as unknown as Embeddings,
|
||||
};
|
||||
});
|
||||
|
||||
return embeddingModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Gemini embeddings model: ${err}`);
|
||||
console.error(`Error loading OpenAI embeddings models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
||||
@ -1,6 +1,82 @@
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
import { getGroqApiKey } from '../../config';
|
||||
import logger from '../../utils/logger';
|
||||
import { getGroqApiKey } from '../config';
|
||||
import { ChatModel } from '.';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
|
||||
const groqChatModels: Record<string, string>[] = [
|
||||
{
|
||||
displayName: 'Gemma2 9B IT',
|
||||
key: 'gemma2-9b-it',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama 3.3 70B Versatile',
|
||||
key: 'llama-3.3-70b-versatile',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama 3.1 8B Instant',
|
||||
key: 'llama-3.1-8b-instant',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama3 70B 8192',
|
||||
key: 'llama3-70b-8192',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama3 8B 8192',
|
||||
key: 'llama3-8b-8192',
|
||||
},
|
||||
{
|
||||
displayName: 'Mixtral 8x7B 32768',
|
||||
key: 'mixtral-8x7b-32768',
|
||||
},
|
||||
{
|
||||
displayName: 'Qwen QWQ 32B (Preview)',
|
||||
key: 'qwen-qwq-32b',
|
||||
},
|
||||
{
|
||||
displayName: 'Mistral Saba 24B (Preview)',
|
||||
key: 'mistral-saba-24b',
|
||||
},
|
||||
{
|
||||
displayName: 'Qwen 2.5 Coder 32B (Preview)',
|
||||
key: 'qwen-2.5-coder-32b',
|
||||
},
|
||||
{
|
||||
displayName: 'Qwen 2.5 32B (Preview)',
|
||||
key: 'qwen-2.5-32b',
|
||||
},
|
||||
{
|
||||
displayName: 'DeepSeek R1 Distill Qwen 32B (Preview)',
|
||||
key: 'deepseek-r1-distill-qwen-32b',
|
||||
},
|
||||
{
|
||||
displayName: 'DeepSeek R1 Distill Llama 70B SpecDec (Preview)',
|
||||
key: 'deepseek-r1-distill-llama-70b-specdec',
|
||||
},
|
||||
{
|
||||
displayName: 'DeepSeek R1 Distill Llama 70B (Preview)',
|
||||
key: 'deepseek-r1-distill-llama-70b',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama 3.3 70B SpecDec (Preview)',
|
||||
key: 'llama-3.3-70b-specdec',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama 3.2 1B Preview (Preview)',
|
||||
key: 'llama-3.2-1b-preview',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama 3.2 3B Preview (Preview)',
|
||||
key: 'llama-3.2-3b-preview',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama 3.2 11B Vision Preview (Preview)',
|
||||
key: 'llama-3.2-11b-vision-preview',
|
||||
},
|
||||
{
|
||||
displayName: 'Llama 3.2 90B Vision Preview (Preview)',
|
||||
key: 'llama-3.2-90b-vision-preview',
|
||||
},
|
||||
];
|
||||
|
||||
export const loadGroqChatModels = async () => {
|
||||
const groqApiKey = getGroqApiKey();
|
||||
@ -8,129 +84,25 @@ export const loadGroqChatModels = async () => {
|
||||
if (!groqApiKey) return {};
|
||||
|
||||
try {
|
||||
const chatModels = {
|
||||
'llama-3.3-70b-versatile': {
|
||||
displayName: 'Llama 3.3 70B',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'llama-3.3-70b-versatile',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
const chatModels: Record<string, ChatModel> = {};
|
||||
|
||||
groqChatModels.forEach((model) => {
|
||||
chatModels[model.key] = {
|
||||
displayName: model.displayName,
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: model.key,
|
||||
temperature: 0.7,
|
||||
configuration: {
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'llama-3.2-3b-preview': {
|
||||
displayName: 'Llama 3.2 3B',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'llama-3.2-3b-preview',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'llama-3.2-11b-vision-preview': {
|
||||
displayName: 'Llama 3.2 11B Vision',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'llama-3.2-11b-vision-preview',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'llama-3.2-90b-vision-preview': {
|
||||
displayName: 'Llama 3.2 90B Vision',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'llama-3.2-90b-vision-preview',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'llama-3.1-8b-instant': {
|
||||
displayName: 'Llama 3.1 8B',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'llama-3.1-8b-instant',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'llama3-8b-8192': {
|
||||
displayName: 'LLaMA3 8B',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'llama3-8b-8192',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'llama3-70b-8192': {
|
||||
displayName: 'LLaMA3 70B',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'llama3-70b-8192',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'mixtral-8x7b-32768': {
|
||||
displayName: 'Mixtral 8x7B',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'mixtral-8x7b-32768',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
'gemma2-9b-it': {
|
||||
displayName: 'Gemma2 9B',
|
||||
model: new ChatOpenAI(
|
||||
{
|
||||
openAIApiKey: groqApiKey,
|
||||
modelName: 'gemma2-9b-it',
|
||||
temperature: 0.7,
|
||||
},
|
||||
{
|
||||
baseURL: 'https://api.groq.com/openai/v1',
|
||||
},
|
||||
),
|
||||
},
|
||||
};
|
||||
}) as unknown as BaseChatModel,
|
||||
};
|
||||
});
|
||||
|
||||
return chatModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Groq models: ${err}`);
|
||||
console.error(`Error loading Groq models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
||||
@ -1,33 +1,49 @@
|
||||
import { loadGroqChatModels } from './groq';
|
||||
import { loadOllamaChatModels, loadOllamaEmbeddingsModels } from './ollama';
|
||||
import { loadOpenAIChatModels, loadOpenAIEmbeddingsModels } from './openai';
|
||||
import { loadAnthropicChatModels } from './anthropic';
|
||||
import { loadTransformersEmbeddingsModels } from './transformers';
|
||||
import { loadGeminiChatModels, loadGeminiEmbeddingsModels } from './gemini';
|
||||
import { Embeddings } from '@langchain/core/embeddings';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { loadOpenAIChatModels, loadOpenAIEmbeddingModels } from './openai';
|
||||
import {
|
||||
getCustomOpenaiApiKey,
|
||||
getCustomOpenaiApiUrl,
|
||||
getCustomOpenaiModelName,
|
||||
} from '../../config';
|
||||
} from '../config';
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
import { loadOllamaChatModels, loadOllamaEmbeddingModels } from './ollama';
|
||||
import { loadGroqChatModels } from './groq';
|
||||
import { loadAnthropicChatModels } from './anthropic';
|
||||
import { loadGeminiChatModels, loadGeminiEmbeddingModels } from './gemini';
|
||||
|
||||
const chatModelProviders = {
|
||||
export interface ChatModel {
|
||||
displayName: string;
|
||||
model: BaseChatModel;
|
||||
}
|
||||
|
||||
export interface EmbeddingModel {
|
||||
displayName: string;
|
||||
model: Embeddings;
|
||||
}
|
||||
|
||||
export const chatModelProviders: Record<
|
||||
string,
|
||||
() => Promise<Record<string, ChatModel>>
|
||||
> = {
|
||||
openai: loadOpenAIChatModels,
|
||||
groq: loadGroqChatModels,
|
||||
ollama: loadOllamaChatModels,
|
||||
groq: loadGroqChatModels,
|
||||
anthropic: loadAnthropicChatModels,
|
||||
gemini: loadGeminiChatModels,
|
||||
};
|
||||
|
||||
const embeddingModelProviders = {
|
||||
openai: loadOpenAIEmbeddingsModels,
|
||||
local: loadTransformersEmbeddingsModels,
|
||||
ollama: loadOllamaEmbeddingsModels,
|
||||
gemini: loadGeminiEmbeddingsModels,
|
||||
export const embeddingModelProviders: Record<
|
||||
string,
|
||||
() => Promise<Record<string, EmbeddingModel>>
|
||||
> = {
|
||||
openai: loadOpenAIEmbeddingModels,
|
||||
ollama: loadOllamaEmbeddingModels,
|
||||
gemini: loadGeminiEmbeddingModels,
|
||||
};
|
||||
|
||||
export const getAvailableChatModelProviders = async () => {
|
||||
const models = {};
|
||||
const models: Record<string, Record<string, ChatModel>> = {};
|
||||
|
||||
for (const provider in chatModelProviders) {
|
||||
const providerModels = await chatModelProviders[provider]();
|
||||
@ -52,7 +68,7 @@ export const getAvailableChatModelProviders = async () => {
|
||||
configuration: {
|
||||
baseURL: customOpenAiApiUrl,
|
||||
},
|
||||
}),
|
||||
}) as unknown as BaseChatModel,
|
||||
},
|
||||
}
|
||||
: {}),
|
||||
@ -62,7 +78,7 @@ export const getAvailableChatModelProviders = async () => {
|
||||
};
|
||||
|
||||
export const getAvailableEmbeddingModelProviders = async () => {
|
||||
const models = {};
|
||||
const models: Record<string, Record<string, EmbeddingModel>> = {};
|
||||
|
||||
for (const provider in embeddingModelProviders) {
|
||||
const providerModels = await embeddingModelProviders[provider]();
|
||||
|
||||
@ -1,74 +1,73 @@
|
||||
import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
|
||||
import { getKeepAlive, getOllamaApiEndpoint } from '../../config';
|
||||
import logger from '../../utils/logger';
|
||||
import { ChatOllama } from '@langchain/community/chat_models/ollama';
|
||||
import axios from 'axios';
|
||||
import { getKeepAlive, getOllamaApiEndpoint } from '../config';
|
||||
import { ChatModel, EmbeddingModel } from '.';
|
||||
import { ChatOllama } from '@langchain/community/chat_models/ollama';
|
||||
import { OllamaEmbeddings } from '@langchain/community/embeddings/ollama';
|
||||
|
||||
export const loadOllamaChatModels = async () => {
|
||||
const ollamaEndpoint = getOllamaApiEndpoint();
|
||||
const keepAlive = getKeepAlive();
|
||||
const ollamaApiEndpoint = getOllamaApiEndpoint();
|
||||
|
||||
if (!ollamaEndpoint) return {};
|
||||
if (!ollamaApiEndpoint) return {};
|
||||
|
||||
try {
|
||||
const response = await axios.get(`${ollamaEndpoint}/api/tags`, {
|
||||
const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
});
|
||||
|
||||
const { models: ollamaModels } = response.data;
|
||||
const { models } = res.data;
|
||||
|
||||
const chatModels = ollamaModels.reduce((acc, model) => {
|
||||
acc[model.model] = {
|
||||
const chatModels: Record<string, ChatModel> = {};
|
||||
|
||||
models.forEach((model: any) => {
|
||||
chatModels[model.model] = {
|
||||
displayName: model.name,
|
||||
model: new ChatOllama({
|
||||
baseUrl: ollamaEndpoint,
|
||||
baseUrl: ollamaApiEndpoint,
|
||||
model: model.model,
|
||||
temperature: 0.7,
|
||||
keepAlive: keepAlive,
|
||||
keepAlive: getKeepAlive(),
|
||||
}),
|
||||
};
|
||||
|
||||
return acc;
|
||||
}, {});
|
||||
});
|
||||
|
||||
return chatModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Ollama models: ${err}`);
|
||||
console.error(`Error loading Ollama models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
||||
export const loadOllamaEmbeddingsModels = async () => {
|
||||
const ollamaEndpoint = getOllamaApiEndpoint();
|
||||
export const loadOllamaEmbeddingModels = async () => {
|
||||
const ollamaApiEndpoint = getOllamaApiEndpoint();
|
||||
|
||||
if (!ollamaEndpoint) return {};
|
||||
if (!ollamaApiEndpoint) return {};
|
||||
|
||||
try {
|
||||
const response = await axios.get(`${ollamaEndpoint}/api/tags`, {
|
||||
const res = await axios.get(`${ollamaApiEndpoint}/api/tags`, {
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
});
|
||||
|
||||
const { models: ollamaModels } = response.data;
|
||||
const { models } = res.data;
|
||||
|
||||
const embeddingsModels = ollamaModels.reduce((acc, model) => {
|
||||
acc[model.model] = {
|
||||
const embeddingModels: Record<string, EmbeddingModel> = {};
|
||||
|
||||
models.forEach((model: any) => {
|
||||
embeddingModels[model.model] = {
|
||||
displayName: model.name,
|
||||
model: new OllamaEmbeddings({
|
||||
baseUrl: ollamaEndpoint,
|
||||
baseUrl: ollamaApiEndpoint,
|
||||
model: model.model,
|
||||
}),
|
||||
};
|
||||
});
|
||||
|
||||
return acc;
|
||||
}, {});
|
||||
|
||||
return embeddingsModels;
|
||||
return embeddingModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Ollama embeddings model: ${err}`);
|
||||
console.error(`Error loading Ollama embeddings models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
||||
@ -1,89 +1,90 @@
|
||||
import { ChatOpenAI, OpenAIEmbeddings } from '@langchain/openai';
|
||||
import { getOpenaiApiKey } from '../../config';
|
||||
import logger from '../../utils/logger';
|
||||
import { getOpenaiApiKey } from '../config';
|
||||
import { ChatModel, EmbeddingModel } from '.';
|
||||
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
|
||||
import { Embeddings } from '@langchain/core/embeddings';
|
||||
|
||||
const openaiChatModels: Record<string, string>[] = [
|
||||
{
|
||||
displayName: 'GPT-3.5 Turbo',
|
||||
key: 'gpt-3.5-turbo',
|
||||
},
|
||||
{
|
||||
displayName: 'GPT-4',
|
||||
key: 'gpt-4',
|
||||
},
|
||||
{
|
||||
displayName: 'GPT-4 turbo',
|
||||
key: 'gpt-4-turbo',
|
||||
},
|
||||
{
|
||||
displayName: 'GPT-4 omni',
|
||||
key: 'gpt-4o',
|
||||
},
|
||||
{
|
||||
displayName: 'GPT-4 omni mini',
|
||||
key: 'gpt-4o-mini',
|
||||
},
|
||||
];
|
||||
|
||||
const openaiEmbeddingModels: Record<string, string>[] = [
|
||||
{
|
||||
displayName: 'Text Embedding 3 Small',
|
||||
key: 'text-embedding-3-small',
|
||||
},
|
||||
{
|
||||
displayName: 'Text Embedding 3 Large',
|
||||
key: 'text-embedding-3-large',
|
||||
},
|
||||
];
|
||||
|
||||
export const loadOpenAIChatModels = async () => {
|
||||
const openAIApiKey = getOpenaiApiKey();
|
||||
const openaiApiKey = getOpenaiApiKey();
|
||||
|
||||
if (!openAIApiKey) return {};
|
||||
if (!openaiApiKey) return {};
|
||||
|
||||
try {
|
||||
const chatModels = {
|
||||
'gpt-3.5-turbo': {
|
||||
displayName: 'GPT-3.5 Turbo',
|
||||
const chatModels: Record<string, ChatModel> = {};
|
||||
|
||||
openaiChatModels.forEach((model) => {
|
||||
chatModels[model.key] = {
|
||||
displayName: model.displayName,
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey,
|
||||
modelName: 'gpt-3.5-turbo',
|
||||
openAIApiKey: openaiApiKey,
|
||||
modelName: model.key,
|
||||
temperature: 0.7,
|
||||
}),
|
||||
},
|
||||
'gpt-4': {
|
||||
displayName: 'GPT-4',
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey,
|
||||
modelName: 'gpt-4',
|
||||
temperature: 0.7,
|
||||
}),
|
||||
},
|
||||
'gpt-4-turbo': {
|
||||
displayName: 'GPT-4 turbo',
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey,
|
||||
modelName: 'gpt-4-turbo',
|
||||
temperature: 0.7,
|
||||
}),
|
||||
},
|
||||
'gpt-4o': {
|
||||
displayName: 'GPT-4 omni',
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey,
|
||||
modelName: 'gpt-4o',
|
||||
temperature: 0.7,
|
||||
}),
|
||||
},
|
||||
'gpt-4o-mini': {
|
||||
displayName: 'GPT-4 omni mini',
|
||||
model: new ChatOpenAI({
|
||||
openAIApiKey,
|
||||
modelName: 'gpt-4o-mini',
|
||||
temperature: 0.7,
|
||||
}),
|
||||
},
|
||||
};
|
||||
}) as unknown as BaseChatModel,
|
||||
};
|
||||
});
|
||||
|
||||
return chatModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading OpenAI models: ${err}`);
|
||||
console.error(`Error loading OpenAI models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
||||
export const loadOpenAIEmbeddingsModels = async () => {
|
||||
const openAIApiKey = getOpenaiApiKey();
|
||||
export const loadOpenAIEmbeddingModels = async () => {
|
||||
const openaiApiKey = getOpenaiApiKey();
|
||||
|
||||
if (!openAIApiKey) return {};
|
||||
if (!openaiApiKey) return {};
|
||||
|
||||
try {
|
||||
const embeddingModels = {
|
||||
'text-embedding-3-small': {
|
||||
displayName: 'Text Embedding 3 Small',
|
||||
const embeddingModels: Record<string, EmbeddingModel> = {};
|
||||
|
||||
openaiEmbeddingModels.forEach((model) => {
|
||||
embeddingModels[model.key] = {
|
||||
displayName: model.displayName,
|
||||
model: new OpenAIEmbeddings({
|
||||
openAIApiKey,
|
||||
modelName: 'text-embedding-3-small',
|
||||
}),
|
||||
},
|
||||
'text-embedding-3-large': {
|
||||
displayName: 'Text Embedding 3 Large',
|
||||
model: new OpenAIEmbeddings({
|
||||
openAIApiKey,
|
||||
modelName: 'text-embedding-3-large',
|
||||
}),
|
||||
},
|
||||
};
|
||||
openAIApiKey: openaiApiKey,
|
||||
modelName: model.key,
|
||||
}) as unknown as Embeddings,
|
||||
};
|
||||
});
|
||||
|
||||
return embeddingModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading OpenAI embeddings model: ${err}`);
|
||||
console.error(`Error loading OpenAI embeddings models: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
|
||||
@ -1,32 +0,0 @@
|
||||
import logger from '../../utils/logger';
|
||||
import { HuggingFaceTransformersEmbeddings } from '../huggingfaceTransformer';
|
||||
|
||||
export const loadTransformersEmbeddingsModels = async () => {
|
||||
try {
|
||||
const embeddingModels = {
|
||||
'xenova-bge-small-en-v1.5': {
|
||||
displayName: 'BGE Small',
|
||||
model: new HuggingFaceTransformersEmbeddings({
|
||||
modelName: 'Xenova/bge-small-en-v1.5',
|
||||
}),
|
||||
},
|
||||
'xenova-gte-small': {
|
||||
displayName: 'GTE Small',
|
||||
model: new HuggingFaceTransformersEmbeddings({
|
||||
modelName: 'Xenova/gte-small',
|
||||
}),
|
||||
},
|
||||
'xenova-bert-base-multilingual-uncased': {
|
||||
displayName: 'Bert Multilingual',
|
||||
model: new HuggingFaceTransformersEmbeddings({
|
||||
modelName: 'Xenova/bert-base-multilingual-uncased',
|
||||
}),
|
||||
},
|
||||
};
|
||||
|
||||
return embeddingModels;
|
||||
} catch (err) {
|
||||
logger.error(`Error loading Transformers embeddings model: ${err}`);
|
||||
return {};
|
||||
}
|
||||
};
|
||||
@ -1,5 +1,5 @@
|
||||
import axios from 'axios';
|
||||
import { getSearxngApiEndpoint } from '../config';
|
||||
import { getSearxngApiEndpoint } from './config';
|
||||
|
||||
interface SearxngSearchOptions {
|
||||
categories?: string[];
|
||||
@ -30,11 +30,12 @@ export const searchSearxng = async (
|
||||
|
||||
if (opts) {
|
||||
Object.keys(opts).forEach((key) => {
|
||||
if (Array.isArray(opts[key])) {
|
||||
url.searchParams.append(key, opts[key].join(','));
|
||||
const value = opts[key as keyof SearxngSearchOptions];
|
||||
if (Array.isArray(value)) {
|
||||
url.searchParams.append(key, value.join(','));
|
||||
return;
|
||||
}
|
||||
url.searchParams.append(key, opts[key]);
|
||||
url.searchParams.append(key, value as string);
|
||||
});
|
||||
}
|
||||
|
||||
|
||||
@ -1,66 +0,0 @@
|
||||
import express from 'express';
|
||||
import logger from '../utils/logger';
|
||||
import db from '../db/index';
|
||||
import { eq } from 'drizzle-orm';
|
||||
import { chats, messages } from '../db/schema';
|
||||
|
||||
const router = express.Router();
|
||||
|
||||
router.get('/', async (_, res) => {
|
||||
try {
|
||||
let chats = await db.query.chats.findMany();
|
||||
|
||||
chats = chats.reverse();
|
||||
|
||||
return res.status(200).json({ chats: chats });
|
||||
} catch (err) {
|
||||
res.status(500).json({ message: 'An error has occurred.' });
|
||||
logger.error(`Error in getting chats: ${err.message}`);
|
||||
}
|
||||
});
|
||||
|
||||
router.get('/:id', async (req, res) => {
|
||||
try {
|
||||
const chatExists = await db.query.chats.findFirst({
|
||||
where: eq(chats.id, req.params.id),
|
||||
});
|
||||
|
||||
if (!chatExists) {
|
||||
return res.status(404).json({ message: 'Chat not found' });
|
||||
}
|
||||
|
||||
const chatMessages = await db.query.messages.findMany({
|
||||
where: eq(messages.chatId, req.params.id),
|
||||
});
|
||||
|
||||
return res.status(200).json({ chat: chatExists, messages: chatMessages });
|
||||
} catch (err) {
|
||||
res.status(500).json({ message: 'An error has occurred.' });
|
||||
logger.error(`Error in getting chat: ${err.message}`);
|
||||
}
|
||||
});
|
||||
|
||||
router.delete(`/:id`, async (req, res) => {
|
||||
try {
|
||||
const chatExists = await db.query.chats.findFirst({
|
||||
where: eq(chats.id, req.params.id),
|
||||
});
|
||||
|
||||
if (!chatExists) {
|
||||
return res.status(404).json({ message: 'Chat not found' });
|
||||
}
|
||||
|
||||
await db.delete(chats).where(eq(chats.id, req.params.id)).execute();
|
||||
await db
|
||||
.delete(messages)
|
||||
.where(eq(messages.chatId, req.params.id))
|
||||
.execute();
|
||||
|
||||
return res.status(200).json({ message: 'Chat deleted successfully' });
|
||||
} catch (err) {
|
||||
res.status(500).json({ message: 'An error has occurred.' });
|
||||
logger.error(`Error in deleting chat: ${err.message}`);
|
||||
}
|
||||
});
|
||||
|
||||
export default router;
|
||||
@ -1,104 +0,0 @@
|
||||
import express from 'express';
|
||||
import {
|
||||
getAvailableChatModelProviders,
|
||||
getAvailableEmbeddingModelProviders,
|
||||
} from '../lib/providers';
|
||||
import {
|
||||
getGroqApiKey,
|
||||
getOllamaApiEndpoint,
|
||||
getAnthropicApiKey,
|
||||
getGeminiApiKey,
|
||||
getOpenaiApiKey,
|
||||
updateConfig,
|
||||
getCustomOpenaiApiUrl,
|
||||
getCustomOpenaiApiKey,
|
||||
getCustomOpenaiModelName,
|
||||
} from '../config';
|
||||
import logger from '../utils/logger';
|
||||
|
||||
const router = express.Router();
|
||||
|
||||
router.get('/', async (_, res) => {
|
||||
try {
|
||||
const config = {};
|
||||
|
||||
const [chatModelProviders, embeddingModelProviders] = await Promise.all([
|
||||
getAvailableChatModelProviders(),
|
||||
getAvailableEmbeddingModelProviders(),
|
||||
]);
|
||||
|
||||
config['chatModelProviders'] = {};
|
||||
config['embeddingModelProviders'] = {};
|
||||
|
||||
for (const provider in chatModelProviders) {
|
||||
config['chatModelProviders'][provider] = Object.keys(
|
||||
chatModelProviders[provider],
|
||||
).map((model) => {
|
||||
return {
|
||||
name: model,
|
||||
displayName: chatModelProviders[provider][model].displayName,
|
||||
};
|
||||
});
|
||||
}
|
||||
|
||||
for (const provider in embeddingModelProviders) {
|
||||
config['embeddingModelProviders'][provider] = Object.keys(
|
||||
embeddingModelProviders[provider],
|
||||
).map((model) => {
|
||||
return {
|
||||
name: model,
|
||||
displayName: embeddingModelProviders[provider][model].displayName,
|
||||
};
|
||||
});
|
||||
}
|
||||
|
||||
config['openaiApiKey'] = getOpenaiApiKey();
|
||||
config['ollamaApiUrl'] = getOllamaApiEndpoint();
|
||||
config['anthropicApiKey'] = getAnthropicApiKey();
|
||||
config['groqApiKey'] = getGroqApiKey();
|
||||
config['geminiApiKey'] = getGeminiApiKey();
|
||||
config['customOpenaiApiUrl'] = getCustomOpenaiApiUrl();
|
||||
config['customOpenaiApiKey'] = getCustomOpenaiApiKey();
|
||||
config['customOpenaiModelName'] = getCustomOpenaiModelName();
|
||||
|
||||
res.status(200).json(config);
|
||||
} catch (err: any) {
|
||||
res.status(500).json({ message: 'An error has occurred.' });
|
||||
logger.error(`Error getting config: ${err.message}`);
|
||||
}
|
||||
});
|
||||
|
||||
router.post('/', async (req, res) => {
|
||||
const config = req.body;
|
||||
|
||||
const updatedConfig = {
|
||||
MODELS: {
|
||||
OPENAI: {
|
||||
API_KEY: config.openaiApiKey,
|
||||
},
|
||||
GROQ: {
|
||||
API_KEY: config.groqApiKey,
|
||||
},
|
||||
ANTHROPIC: {
|
||||
API_KEY: config.anthropicApiKey,
|
||||
},
|
||||
GEMINI: {
|
||||
API_KEY: config.geminiApiKey,
|
||||
},
|
||||
OLLAMA: {
|
||||
API_URL: config.ollamaApiUrl,
|
||||
},
|
||||
CUSTOM_OPENAI: {
|
||||
API_URL: config.customOpenaiApiUrl,
|
||||
API_KEY: config.customOpenaiApiKey,
|
||||
MODEL_NAME: config.customOpenaiModelName,
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
updateConfig(updatedConfig);
|
||||
|
||||
res.status(200).json({ message: 'Config updated' });
|
||||
});
|
||||
|
||||
export default router;
|
||||
@ -1,48 +0,0 @@
|
||||
import express from 'express';
|
||||
import { searchSearxng } from '../lib/searxng';
|
||||
import logger from '../utils/logger';
|
||||
|
||||
const router = express.Router();
|
||||
|
||||
router.get('/', async (req, res) => {
|
||||
try {
|
||||
const data = (
|
||||
await Promise.all([
|
||||
searchSearxng('site:businessinsider.com AI', {
|
||||
engines: ['bing news'],
|
||||
pageno: 1,
|
||||
}),
|
||||
searchSearxng('site:www.exchangewire.com AI', {
|
||||
engines: ['bing news'],
|
||||
pageno: 1,
|
||||
}),
|
||||
searchSearxng('site:yahoo.com AI', {
|
||||
engines: ['bing news'],
|
||||
pageno: 1,
|
||||
}),
|
||||
searchSearxng('site:businessinsider.com tech', {
|
||||
engines: ['bing news'],
|
||||
pageno: 1,
|
||||
}),
|
||||
searchSearxng('site:www.exchangewire.com tech', {
|
||||
engines: ['bing news'],
|
||||
pageno: 1,
|
||||
}),
|
||||
searchSearxng('site:yahoo.com tech', {
|
||||
engines: ['bing news'],
|
||||
pageno: 1,
|
||||
}),
|
||||
])
|
||||
)
|
||||
.map((result) => result.results)
|
||||
.flat()
|
||||
.sort(() => Math.random() - 0.5);
|
||||
|
||||
return res.json({ blogs: data });
|
||||
} catch (err: any) {
|
||||
logger.error(`Error in discover route: ${err.message}`);
|
||||
return res.status(500).json({ message: 'An error has occurred' });
|
||||
}
|
||||
});
|
||||
|
||||
export default router;
|
||||
@ -1,82 +0,0 @@
|
||||
import express from 'express';
|
||||
import handleImageSearch from '../chains/imageSearchAgent';
|
||||
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';
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
import {
|
||||
getCustomOpenaiApiKey,
|
||||
getCustomOpenaiApiUrl,
|
||||
getCustomOpenaiModelName,
|
||||
} from '../config';
|
||||
|
||||
const router = express.Router();
|
||||
|
||||
interface ChatModel {
|
||||
provider: string;
|
||||
model: string;
|
||||
}
|
||||
|
||||
interface ImageSearchBody {
|
||||
query: string;
|
||||
chatHistory: any[];
|
||||
chatModel?: ChatModel;
|
||||
}
|
||||
|
||||
router.post('/', async (req, res) => {
|
||||
try {
|
||||
let body: ImageSearchBody = req.body;
|
||||
|
||||
const chatHistory = body.chatHistory.map((msg: any) => {
|
||||
if (msg.role === 'user') {
|
||||
return new HumanMessage(msg.content);
|
||||
} else if (msg.role === 'assistant') {
|
||||
return new AIMessage(msg.content);
|
||||
}
|
||||
});
|
||||
|
||||
const chatModelProviders = await getAvailableChatModelProviders();
|
||||
|
||||
const chatModelProvider =
|
||||
body.chatModel?.provider || Object.keys(chatModelProviders)[0];
|
||||
const chatModel =
|
||||
body.chatModel?.model ||
|
||||
Object.keys(chatModelProviders[chatModelProvider])[0];
|
||||
|
||||
let llm: BaseChatModel | undefined;
|
||||
|
||||
if (body.chatModel?.provider === 'custom_openai') {
|
||||
llm = new ChatOpenAI({
|
||||
modelName: getCustomOpenaiModelName(),
|
||||
openAIApiKey: getCustomOpenaiApiKey(),
|
||||
temperature: 0.7,
|
||||
configuration: {
|
||||
baseURL: getCustomOpenaiApiUrl(),
|
||||
},
|
||||
}) as unknown as BaseChatModel;
|
||||
} else if (
|
||||
chatModelProviders[chatModelProvider] &&
|
||||
chatModelProviders[chatModelProvider][chatModel]
|
||||
) {
|
||||
llm = chatModelProviders[chatModelProvider][chatModel]
|
||||
.model as unknown as BaseChatModel | undefined;
|
||||
}
|
||||
|
||||
if (!llm) {
|
||||
return res.status(400).json({ message: 'Invalid model selected' });
|
||||
}
|
||||
|
||||
const images = await handleImageSearch(
|
||||
{ query: body.query, chat_history: chatHistory },
|
||||
llm,
|
||||
);
|
||||
|
||||
res.status(200).json({ images });
|
||||
} catch (err) {
|
||||
res.status(500).json({ message: 'An error has occurred.' });
|
||||
logger.error(`Error in image search: ${err.message}`);
|
||||
}
|
||||
});
|
||||
|
||||
export default router;
|
||||
@ -1,24 +0,0 @@
|
||||
import express from 'express';
|
||||
import imagesRouter from './images';
|
||||
import videosRouter from './videos';
|
||||
import configRouter from './config';
|
||||
import modelsRouter from './models';
|
||||
import suggestionsRouter from './suggestions';
|
||||
import chatsRouter from './chats';
|
||||
import searchRouter from './search';
|
||||
import discoverRouter from './discover';
|
||||
import uploadsRouter from './uploads';
|
||||
|
||||
const router = express.Router();
|
||||
|
||||
router.use('/images', imagesRouter);
|
||||
router.use('/videos', videosRouter);
|
||||
router.use('/config', configRouter);
|
||||
router.use('/models', modelsRouter);
|
||||
router.use('/suggestions', suggestionsRouter);
|
||||
router.use('/chats', chatsRouter);
|
||||
router.use('/search', searchRouter);
|
||||
router.use('/discover', discoverRouter);
|
||||
router.use('/uploads', uploadsRouter);
|
||||
|
||||
export default router;
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user