mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-11-15 10:50:31 +01:00
332 lines
11 KiB
JavaScript
332 lines
11 KiB
JavaScript
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const { v4: uuidv4 } = require("uuid");
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const { Document } = require("../../../models/documents");
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const { Telemetry } = require("../../../models/telemetry");
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const { Workspace } = require("../../../models/workspace");
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const {
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getLLMProvider,
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getEmbeddingEngineSelection,
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} = require("../../../utils/helpers");
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const { reqBody } = require("../../../utils/http");
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const { validApiKey } = require("../../../utils/middleware/validApiKey");
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const { EventLogs } = require("../../../models/eventLogs");
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const {
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OpenAICompatibleChat,
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} = require("../../../utils/chats/openaiCompatible");
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function apiOpenAICompatibleEndpoints(app) {
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if (!app) return;
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app.get("/v1/openai/models", [validApiKey], async (request, response) => {
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/*
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#swagger.tags = ['OpenAI Compatible Endpoints']
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#swagger.description = 'Get all available "models" which are workspaces you can use for chatting.'
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#swagger.responses[200] = {
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content: {
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"application/json": {
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"schema": {
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"type": "object",
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"example": {
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"models": [
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{
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"name": "Sample workspace",
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"model": "sample-workspace",
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"llm": {
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"provider": "ollama",
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"model": "llama3:8b"
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}
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},
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{
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"name": "Second workspace",
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"model": "workspace-2",
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"llm": {
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"provider": "openai",
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"model": "gpt-3.5-turbo"
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}
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}
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]
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}
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}
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}
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}
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}
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#swagger.responses[403] = {
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schema: {
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"$ref": "#/definitions/InvalidAPIKey"
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}
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}
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*/
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try {
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const data = [];
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const workspaces = await Workspace.where();
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for (const workspace of workspaces) {
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const provider = workspace?.chatProvider ?? process.env.LLM_PROVIDER;
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let LLMProvider = getLLMProvider({
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provider,
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model: workspace?.chatModel,
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});
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data.push({
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name: workspace.name,
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model: workspace.slug,
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llm: {
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provider: provider,
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model: LLMProvider.model,
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},
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});
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}
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return response.status(200).json({ data });
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} catch (e) {
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console.log(e.message, e);
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response.sendStatus(500).end();
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}
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});
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app.post(
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"/v1/openai/chat/completions",
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[validApiKey],
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async (request, response) => {
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/*
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#swagger.tags = ['OpenAI Compatible Endpoints']
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#swagger.description = 'Execute a chat with a workspace with OpenAI compatibility. Supports streaming as well. Model must be a workspace slug from /models.'
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#swagger.requestBody = {
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description: 'Send a prompt to the workspace with full use of documents as if sending a chat in AnythingLLM. Only supports some values of OpenAI API. See example below.',
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required: true,
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type: 'object',
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content: {
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"application/json": {
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example: {
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messages: [
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{"role":"system", content: "You are a helpful assistant"},
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{"role":"user", content: "What is AnythingLLM?"},
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{"role":"assistant", content: "AnythingLLM is...."},
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{"role":"user", content: "Follow up question..."}
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],
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model: "sample-workspace",
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stream: true,
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temperature: 0.7
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}
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}
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}
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}
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#swagger.responses[403] = {
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schema: {
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"$ref": "#/definitions/InvalidAPIKey"
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}
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}
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*/
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try {
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const {
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model,
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messages = [],
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temperature,
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stream = false,
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} = reqBody(request);
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const workspace = await Workspace.get({ slug: String(model) });
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if (!workspace) return response.status(401).end();
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const userMessage = messages.pop();
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if (userMessage.role !== "user") {
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return response.status(400).json({
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id: uuidv4(),
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type: "abort",
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textResponse: null,
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sources: [],
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close: true,
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error:
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"No user prompt found. Must be last element in message array with 'user' role.",
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});
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}
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const systemPrompt =
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messages.find((chat) => chat.role === "system")?.content ?? null;
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const history = messages.filter((chat) => chat.role !== "system") ?? [];
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if (!stream) {
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const chatResult = await OpenAICompatibleChat.chatSync({
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workspace,
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systemPrompt,
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history,
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prompt: userMessage.content,
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temperature: Number(temperature),
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});
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await Telemetry.sendTelemetry("sent_chat", {
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LLMSelection:
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workspace.chatProvider ?? process.env.LLM_PROVIDER ?? "openai",
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Embedder: process.env.EMBEDDING_ENGINE || "inherit",
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VectorDbSelection: process.env.VECTOR_DB || "lancedb",
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});
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await EventLogs.logEvent("api_sent_chat", {
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workspaceName: workspace?.name,
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chatModel: workspace?.chatModel || "System Default",
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});
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return response.status(200).json(chatResult);
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}
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response.setHeader("Cache-Control", "no-cache");
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response.setHeader("Content-Type", "text/event-stream");
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response.setHeader("Access-Control-Allow-Origin", "*");
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response.setHeader("Connection", "keep-alive");
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response.flushHeaders();
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await OpenAICompatibleChat.streamChat({
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workspace,
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systemPrompt,
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history,
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prompt: userMessage.content,
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temperature: Number(temperature),
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response,
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});
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await Telemetry.sendTelemetry("sent_chat", {
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LLMSelection: process.env.LLM_PROVIDER || "openai",
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Embedder: process.env.EMBEDDING_ENGINE || "inherit",
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VectorDbSelection: process.env.VECTOR_DB || "lancedb",
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});
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await EventLogs.logEvent("api_sent_chat", {
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workspaceName: workspace?.name,
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chatModel: workspace?.chatModel || "System Default",
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});
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response.end();
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} catch (e) {
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console.log(e.message, e);
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response.status(500).end();
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}
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}
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);
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app.post(
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"/v1/openai/embeddings",
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[validApiKey],
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async (request, response) => {
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/*
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#swagger.tags = ['OpenAI Compatible Endpoints']
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#swagger.description = 'Get the embeddings of any arbitrary text string. This will use the embedder provider set in the system. Please ensure the token length of each string fits within the context of your embedder model.'
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#swagger.requestBody = {
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description: 'The input string(s) to be embedded. If the text is too long for the embedder model context, it will fail to embed. The vector and associated chunk metadata will be returned in the array order provided',
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required: true,
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type: 'object',
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content: {
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"application/json": {
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example: {
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input: [
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"This is my first string to embed",
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"This is my second string to embed",
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],
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model: null,
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}
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}
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}
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}
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#swagger.responses[403] = {
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schema: {
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"$ref": "#/definitions/InvalidAPIKey"
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}
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}
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*/
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try {
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const { inputs = [] } = reqBody(request);
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const validArray = inputs.every((input) => typeof input === "string");
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if (!validArray)
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throw new Error("All inputs to be embedded must be strings.");
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const Embedder = getEmbeddingEngineSelection();
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const embeddings = await Embedder.embedChunks(inputs);
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const data = [];
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embeddings.forEach((embedding, index) => {
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data.push({
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object: "embedding",
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embedding,
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index,
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});
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});
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return response.status(200).json({
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object: "list",
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data,
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model: Embedder.model,
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});
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} catch (e) {
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console.log(e.message, e);
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response.status(500).end();
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}
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}
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);
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app.get(
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"/v1/openai/vector_stores",
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[validApiKey],
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async (request, response) => {
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/*
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#swagger.tags = ['OpenAI Compatible Endpoints']
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#swagger.description = 'List all the vector database collections connected to AnythingLLM. These are essentially workspaces but return their unique vector db identifier - this is the same as the workspace slug.'
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#swagger.responses[200] = {
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content: {
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"application/json": {
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"schema": {
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"type": "object",
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"example": {
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"data": [
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{
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"id": "slug-here",
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"object": "vector_store",
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"name": "My workspace",
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"file_counts": {
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"total": 3
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},
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"provider": "LanceDB"
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}
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]
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}
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}
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}
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}
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}
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#swagger.responses[403] = {
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schema: {
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"$ref": "#/definitions/InvalidAPIKey"
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}
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}
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*/
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try {
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// We dump all in the first response and despite saying there is
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// not more data the library still checks with a query param so if
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// we detect one - respond with nothing.
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if (Object.keys(request?.query ?? {}).length !== 0) {
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return response.status(200).json({
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data: [],
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has_more: false,
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});
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}
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const data = [];
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const VectorDBProvider = process.env.VECTOR_DB || "lancedb";
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const workspaces = await Workspace.where();
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for (const workspace of workspaces) {
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data.push({
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id: workspace.slug,
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object: "vector_store",
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name: workspace.name,
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file_counts: {
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total: await Document.count({
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workspaceId: Number(workspace.id),
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}),
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},
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provider: VectorDBProvider,
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});
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}
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return response.status(200).json({
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first_id: [...data].splice(0)?.[0]?.id,
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last_id: [...data].splice(-1)?.[0]?.id ?? data.splice(1)?.[0]?.id,
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data,
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has_more: false,
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});
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} catch (e) {
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console.log(e.message, e);
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response.status(500).end();
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}
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}
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);
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}
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module.exports = { apiOpenAICompatibleEndpoints };
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