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diffusion","openai","huggingface","Llama","image generation","speech synthesis","prompt","automation","mistral","gemini","deepseek","framework","mcp","anthropic","claude","ollama","openrouter","groq","openai-compatible","tool-calling","model-context-protocol","coding-agent","agent","vertex-ai","google-cloud","RAG","vector-database","codex","agent-skills"],"repository":{"url":"git+https://github.com/intelligentnode/IntelliNode.git","type":"git"},"description":"JavaScript AI SDK for OpenAI, Claude, Gemini and Vertex AI: chatbots, AI agents, RAG with vector databases, tool calling, MCP, image and speech.","maintainers":[{"name":"barqawiz","email":"barqawicloud@gmail.com"}],"readme":"\n# Intelligent Node (IntelliNode)\n### Unified prompt, evaluation, and production integration to any AI model\n\n\n<p>\n\n<a href=\"https://github.com/Barqawiz/IntelliNode/blob/main/LICENSE\" alt=\"licenses tag\" target=\"_blank\">\n    <img src=\"https://img.shields.io/github/license/intelligentnode/IntelliNode?style=flat-square\" />\n</a>\n\n<a href=\"https://discord.gg/VYgCh2p3Ww\" alt=\"Join our Discord community\">\n    <img src=\"https://img.shields.io/badge/Discord-join%20us-5865F2?style=flat-square&logo=discord&logoColor=white\" />\n</a>\n\n</p>\n\nIntegrate your data with the latest language models and deep learning frameworks using intellinode **javascript**. The library provides intuitive functions for sending input to models like GPT-5.5, Claude, Gemini, WaveNet and Stable diffusion, and receiving generated text, speech, or images. With just a few lines of code, you can easily access the power of cutting-edge AI models to enhance your projects.\n\n# Latest Updates\n- Gemini and Vertex AI: one `GoogleAIWrapper` for the Gemini Developer API and Vertex AI (express mode with an Agent Platform key, projects, ADC), with chat sessions, streaming, Google Search grounding, image generation and editing, Veo video, Lyria music, Gemini speech, the Live API, the Files API and caching. New `vertex` provider for `Chatbot` and `Gen`. ☁️\n- Assistant: build Gemini- or ChatGPT-style apps with saved conversations (memory, files or Firestore), answers grounded on your documents with sources, long-term memory, attachments and streaming. 💬\n- Vector stores behind one interface: Firestore, Vertex AI RAG Engine and Vector Search, Pinecone, Qdrant, Chroma, Weaviate, Milvus, Elasticsearch, pgvector (AlloyDB, Cloud SQL, Supabase) and MongoDB Atlas. 🗂️\n- Agent skill for Claude Code and Codex (`npx intellinode skill install`), and new MCP tools: web search with sources, Gemini images and speech. 🤖\n- Add streaming for Anthropic, GPT-5+, Gemini and Mistral, plus tool calling for OpenAI, Anthropic and Mistral.\n- Gen: 25+ new one-call functions for developers (components, forms, API endpoints, SQL, mock data, regex, tests, code review, SEO meta, UI translation and more) that work the same with every provider. 🧰\n- OpenAI-compatible services (OpenRouter, Groq, DeepSeek, xAI, Together, local Ollama, LM Studio) join OpenAI, Anthropic, Gemini, Mistral, Cohere and NVIDIA, with TypeScript types, timeouts, retries and cancellation. 🔌\n- IntelliNode MCP server: `npx intellinode mcp` gives Claude Code, Cursor and VS Code cross-provider tools. 🧩\n- Coding agent: `new CodingAgent({ workspace })` edits a repository until its tests pass, with any provider. 🛠️\n- Update the default models: GPT-5.5, Claude Sonnet 5, Gemini 3.6 Flash, Mistral Medium, Command A and gpt-image-2. 🚀\n- Fix the frontend bundle: Anthropic browser access, streaming in browsers and Gen templates.\n- Add support for self-hosted vLLM models.\n- Integrated Nvidia-hosted models (DeepSeek and Llama3 🦙).\n- Add Anthropic Claude Fable 5.1 chat.\n\nJoin the [discord server](https://discord.gg/VYgCh2p3Ww) for the latest updates and community support.\n\nChat with your docs via Intellinode one key at [chat.intellinode.ai](https://chat.intellinode.ai/).\n\n# Examples\n## Functions\n\n### Chatbot\n1. imports:\n```js\nconst { Chatbot, ChatGPTInput } = require('intellinode');\n```\n2. call with GPT-5.5 (default):\n```js\n// GPT-5.5 is the default model (low reasoning effort unless set)\nconst input = new ChatGPTInput('You are a helpful assistant.');\ninput.addUserMessage('What is the distance between the Earth and the Moon?');\n\n// get GPT-5.5 responses.\nconst bot = new Chatbot(openaiKey);\nconst responses = await bot.chat(input);\n```\n3. control the reasoning effort:\n```js\n// gpt-5.5 effort: none, low, medium, high, xhigh (gpt-5 also accepts minimal)\nconst input = new ChatGPTInput('You are a helpful assistant.', { \n  model: 'gpt-5.5',\n  effort: 'high'\n});\ninput.addUserMessage('Explain quantum computing');\n\nconst bot = new Chatbot(openaiKey);\nconst responses = await bot.chat(input);\n```\n\n4. stream the response (OpenAI, Anthropic, Gemini, Vertex AI, Mistral, Cohere, NVIDIA and vLLM):\n```js\nfor await (const chunk of bot.stream(input)) {\n  process.stdout.write(chunk);\n}\n```\n5. call tools (the same `tools` option works with `AnthropicInput` and `MistralInput`):\n```js\nconst input = new ChatGPTInput('You are a helpful assistant.', {\n  tools: [{ type: 'function', function: { name: 'get_weather', parameters: { type: 'object', properties: { city: { type: 'string' } } } } }]\n});\ninput.addUserMessage('What is the weather in Paris?');\n\nconst [response] = await bot.chat(input);\n// response.tool_calls[0].function => { name: 'get_weather', arguments: '{\"city\":\"Paris\"}' }\n```\n6. let IntelliNode run the tool loop: the model calls your handlers until it has the answer (same call with `AnthropicInput`, `GeminiInput`, `MistralInput` and `NvidiaInput`):\n```js\nconst tools = [{ name: 'get_weather', description: 'Weather for a city', parameters: { type: 'object', properties: { city: { type: 'string' } } }, handler: async ({ city }) => ({ city, tempC: 22 }) }];\nconst { text, steps } = await bot.runTools(input, tools, { maxSteps: 5 });\n```\n7. get JSON that matches a schema with `new ChatGPTInput('Answer as JSON.', { responseSchema })` and `await bot.chatJson(input)`.\n8. request options for every provider: `new Chatbot(key, 'openai', null, { timeout: 30000, retries: 2, signal: controller.signal })`.\n\n### OpenAI-compatible providers\nOpenRouter, Groq, DeepSeek, xAI, Together, a local Ollama / LM Studio or any endpoint (`openai_compatible` + `baseUrl`) use the same chatbot:\n```js\nconst bot = new Chatbot(process.env.OPENROUTER_API_KEY, 'openrouter');      // or new Chatbot(null, 'ollama', null, { model: 'qwen3' })\nconst input = new OpenAICompatibleInput('You are a helpful assistant.', { model: 'anthropic/claude-sonnet-5' });\ninput.addUserMessage('Who painted the Mona Lisa?');\nconst responses = await bot.chat(input);\n```\n\n### Anthropic Claude Chatbot\n1. imports:\n```js\nconst { Chatbot, AnthropicInput, SupportedChatModels } = require('intellinode');\n```\n2. call (Claude Sonnet 5 is default; use `claude-fable-5-1` for Fable or `claude-opus-5` for Opus):\n```js\nconst input = new AnthropicInput('You are a helpful assistant.');\ninput.addUserMessage('Who painted the Mona Lisa?');\n\nconst claudeBot = new Chatbot(anthropicKey, SupportedChatModels.ANTHROPIC);\nconst responses = await claudeBot.chat(input);\n```\n\n### Google Gemini and Vertex AI Chatbot\n1. imports:\n```js\nconst { Chatbot, GeminiInput } = require('intellinode');\n```\n2. call the Gemini Developer API (key from aistudio.google.com):\n```js\nconst input = new GeminiInput('You are a helpful assistant.', { systemInstruction: true });\ninput.addUserMessage('Who painted the Mona Lisa?');\n\nconst geminiBot = new Chatbot(geminiApiKey, 'gemini');\nconst responses = await geminiBot.chat(input);\n```\n3. or Vertex AI: an Agent Platform key runs in express mode; add `projectId` for project mode, or omit the key to use Application Default Credentials:\n```js\nconst vertexBot = new Chatbot(vertexApiKey, 'vertex');                  // { projectId, location } are optional\ninput.addUserMessage('Describe this image.', [{ data: base64Png, mimeType: 'image/png' }]);\nfor await (const chunk of vertexBot.stream(input)) process.stdout.write(chunk);\n```\n`GeminiInput` also takes `generationConfig` (for example `{ thinkingConfig: { thinkingLevel: 'low' } }`), `tools: [{ googleSearch: {} }]`, `safetySettings` and `cachedContent`.\n\n### Assistant (Gemini- and ChatGPT-style apps)\nA chat assistant with saved conversations, answers from your documents with numbered sources, long-term memory across conversations, attachments, tools and streaming, on any provider:\n```js\nconst { Assistant, FileChatHistory, MemoryVectorStore } = require('intellinode');\n\nconst embedder = { provider: 'vertex', apiKey: vertexApiKey, dimensions: 768 };\nconst assistant = new Assistant({\n  provider: 'vertex', apiKey: vertexApiKey,\n  history: new FileChatHistory({ dir: './data/conversations' }),   // or MemoryChatHistory, FirestoreChatHistory\n  knowledge: new MemoryVectorStore({ embedder, path: './data/knowledge.json' }),\n  memory: new MemoryVectorStore({ embedder, path: './data/memory.json' }),\n  googleSearch: false,                                                // true grounds answers on Google Search\n});\nawait assistant.addDocuments([{ id: 'handbook.md', text: handbookText }]);\n\nconst reply = await assistant.chat('When does the on-call rotation change?', { userId: 'u1' });\n// { conversationId, text, references: [{ index, id, text, score }], citations, memories, usage }\n\nfor await (const event of assistant.stream('And who approves vacations?', { conversationId: reply.conversationId, userId: 'u1' })) {\n  if (event.type === 'text') process.stdout.write(event.text);   // 'start', 'text' chunks, then 'done'\n}\n```\nA complete local app (server and web page) is in [skills/intellinode/assets/chat-app](https://github.com/intelligentnode/IntelliNode/tree/main/IntelliNode/skills/intellinode/assets/chat-app).\n\n### Vector stores and chat history\nEvery store has the same `addDocuments`, `search`, `query`, `upsert` and `delete`, and embeds with any provider:\n```js\nconst { QdrantVectorStore, FirestoreVectorStore, PgVectorStore } = require('intellinode');\nconst store = new QdrantVectorStore({ url: 'http://localhost:6333', collection: 'docs', embedder: { provider: 'openai', apiKey: openaiKey } });\nawait store.addDocuments([{ id: 'a', text: 'IntelliNode supports Gemini.', metadata: { lang: 'en' } }]);\nconst hits = await store.search('Which models are supported?', 3, { lang: 'en' });   // [{ id, score, text, metadata }]\n```\n| Google Cloud | Other |\n| --- | --- |\n| `FirestoreVectorStore`, `VertexRAGStore` (RAG Engine), `VertexVectorSearchStore` (Vector Search 2.0), `VertexVectorSearchIndexStore`, `PgVectorStore` on AlloyDB or Cloud SQL | `MemoryVectorStore`, `PineconeVectorStore`, `QdrantVectorStore`, `ChromaVectorStore`, `WeaviateVectorStore`, `MilvusVectorStore`, `ElasticsearchVectorStore`, `PgVectorStore`, `MongoDBAtlasVectorStore` |\n\nConversations: `MemoryChatHistory`, `FileChatHistory` and `FirestoreChatHistory`. The Google Cloud stores use OAuth (`gcloud auth application-default login`, a service account or `accessToken`) because those APIs do not take API keys.\n\nThe documentation on how to switch between ChatGPT, Mistral, Anthropic, and LLama can be found in the [IntelliNode Wiki](https://www.intellinode.ai/docs/npm/chatbot/get-started).\n\n### Semantic Search\n1. imports:\n```js\nconst { SemanticSearch } = require('intellinode');\n```\n2. call:\n```js\nconst search = new SemanticSearch(apiKey);\n// pivotItem: item to search.\nconst results = await search.getTopMatches(pivotItem, searchArray, numberOfMatches);\nconst filteredArray = search.filterTopMatches(results, searchArray)\n```\n### Gen\nOne-call functions for the tasks web developers hand to AI every day. Every function takes the same\narguments `(input, apiKey, provider, options)` and works with `openai`, `anthropic`, `gemini`,\n`mistral`, `cohere`, `nvidia` and the OpenAI-compatible providers (`openrouter`, `groq`, `deepseek`,\n`xai`, `together`, `ollama`, `lmstudio`): change the provider name and the key, keep the code.\n\n1. imports:\n```js\nconst { Gen } = require('intellinode');\n```\n2. build UI:\n```js\n// React + TypeScript + Tailwind component source (framework: react | vue | svelte | angular | html)\nconst code = await Gen.generate_component('a pricing card with a plan name, price and a CTA button', openaiKey, 'openai',\n  { framework: 'react', language: 'typescript', styling: 'tailwind' });\n\n// an accessible form with client-side validation\nconst form = await Gen.generate_form('a contact form with name, email and message', anthropicKey, 'anthropic');\n\n// a page section, a stylesheet, a responsive HTML email, an SVG icon or a color palette\nconst hero = await Gen.generate_page_section('a hero for a note-taking app', openaiKey, 'openai', { sectionType: 'hero' });\nconst css = await Gen.generate_css('a responsive three column card grid', openaiKey);\nconst email = await Gen.generate_email_template('a welcome email with a \"Get started\" button', openaiKey);\nconst icon = await Gen.generate_svg_icon('a shopping cart', openaiKey);\nconst palette = await Gen.generate_color_palette('a calm fintech dashboard', openaiKey, 'openai', { count: 5 });\n\n// design tokens: 11-step color scales, light/dark roles, WCAG contrast, CSS variables and a Tailwind theme\nconst tokens = await Gen.generate_design_tokens('a calm fintech dashboard', openaiKey, 'openai', { brandColor: '#4F46E5' });\n// tokens.css -> ':root { --color-primary-500: #4f46e5; ... }', tokens.tailwind.theme.extend.colors\n\n// fix accessibility problems: { html, issues: [{ issue, fix, wcag }] }\nconst fixed = await Gen.improve_accessibility('<img src=\"hero.jpg\"><a href=\"/x\">click here</a>', openaiKey);\n\n// full page or data dashboard as { html, message }\nconst page = await Gen.generate_html_page('a registration page with a flat modern theme', openaiKey);\nconst dashboard = await Gen.generate_dashboard(csvString, 'website growth', openaiKey, undefined, 2);\n```\n3. backend and developer workflow:\n```js\nconst endpoint = await Gen.generate_api_endpoint('POST /api/todos that creates a todo', openaiKey, 'openai', { framework: 'express' });\nconst sql = await Gen.generate_sql('top 10 customers by order total', openaiKey, 'openai', { dialect: 'postgresql', schema });\nconst schema = await Gen.generate_json_schema('a blog post with title, slug, tags and author', openaiKey);\nconst openapi = await Gen.generate_openapi_spec(expressRouterCode, openaiKey, 'openai', { title: 'Users API' }); // OpenAPI 3.1 object\nconst rows = await Gen.generate_mock_data('a user with id, fullName, email and role', openaiKey, 'openai', { count: 20 });\nconst regex = await Gen.generate_regex('a US phone number', openaiKey);      // { pattern, flags, regex, matches, nonMatches }\nconst tests = await Gen.generate_unit_tests(code, openaiKey, 'openai', { framework: 'jest', modulePath: './math' });\nconst review = await Gen.review_code(code, openaiKey);                        // { summary, score, issues }\nconst fix = await Gen.fix_code(code, openaiKey, 'openai', { problem: 'average([1,2,3]) returns NaN' });\nconst explained = await Gen.explain_code(code, openaiKey, 'openai', { audience: 'junior developer' });\nconst ts = await Gen.convert_code(code, openaiKey, 'openai', { from: 'JavaScript', to: 'TypeScript' });\nconst commit = await Gen.generate_commit_message(gitDiff, openaiKey);        // conventional commit\nconst readme = await Gen.generate_readme('intellinode-cli: generates web components from a prompt', openaiKey);\nconst notes = await Gen.generate_release_notes(changes, openaiKey, 'openai', { version: '2.4.0' });\n```\n4. content and metadata:\n```js\nconst meta = await Gen.generate_seo_meta('a product page for wireless headphones', openaiKey, 'openai', { url, siteName });\nconst spanish = await Gen.translate_ui_strings({ save: 'Save', greeting: 'Hello, {name}!' }, openaiKey, 'openai', { targetLanguage: 'Spanish' });\nconst faq = await Gen.generate_faq('a specialty coffee subscription', openaiKey, 'openai', { count: 5 });\nconst copy = await Gen.generate_landing_copy('an AI meeting assistant', openaiKey);\nconst blogPost = await Gen.get_blog_post(prompt, openaiKey);\nconst description = await Gen.get_marketing_desc('an ergonomic gaming chair', openaiKey);\nconst text = await Gen.generate_text('any prompt', anthropicKey, 'anthropic', { system: 'You are terse.' });\nconst data = await Gen.generate_json('Where is the Eiffel Tower?', { type: 'object', properties: { city: { type: 'string' } } }, openaiKey);\n```\nCode functions return the code as a string (no markdown fences); structured functions return parsed\nobjects. Pass `options.model` to pick a model, `options.maxTokens` or `options.temperature` to tune it, and\n`options.timeout`, `options.retries` or `options.signal` to control the request.\n\n## Models Access\n### Gemini and Vertex AI\n`GoogleAIWrapper` reaches every Gemini feature on the Developer API and on Vertex AI:\n```js\nconst { GoogleAIWrapper } = require('intellinode');\nconst google = new GoogleAIWrapper(vertexApiKey, { vertex: true });     // or new GoogleAIWrapper(geminiApiKey)\n\nconst chat = google.startChat({ systemInstruction: 'Be brief.' });     // keeps history and thought signatures\nawait chat.sendText('My name is Sam.');\n\nconst grounded = await google.generateContent({ contents: 'Latest Node.js LTS?', tools: [{ googleSearch: {} }] });\nGoogleAIWrapper.extractCitations(grounded);                            // [{ title, uri }]\n\nconst [image] = GoogleAIWrapper.extractImages(await google.generateImage('A watercolor fox'));\nconst wav = await google.textToSpeech('Welcome to IntelliNode', { voice: 'Puck' });\nconst vectors = await google.embedTexts(['first', 'second'], null, { taskType: 'RETRIEVAL_DOCUMENT' });\nawait google.mediaToText('Summarize this report.', ['report.pdf']);\n```\nProject mode (`{ vertex: true, projectId }`) adds Veo video (`generateVideo`), Lyria music (`generateMusic`), the Live API (`liveConnect`, `liveGenerate`) and Agent Engine. The Developer API adds the Files API and context caching. `GoogleAIWrapper` keeps the Google Cloud Text-to-Speech, Speech-to-Text, Vision, Natural Language and Translation calls.\n\n### Image models\n\n1. imports:\n```js\nconst { RemoteImageModel, SupportedImageModels, ImageModelInput } = require('intellinode');\n```\n\n2. call OpenAI (gpt-image-2 is default):\n```js\nprovider=SupportedImageModels.OPENAI;\n\nconst imgModel = new RemoteImageModel(apiKey, provider);\nconst images = await imgModel.generateImages(new ImageModelInput({\n    prompt: 'teddy writing a blog in times square',\n    numberOfImages: 1\n}));\n```\n\n3. change to call Stable Diffusion or Gemini (`'gemini'`, or `'vertex'` with an Agent Platform key):\n```js\nprovider=SupportedImageModels.STABILITY;\n// ... same code\n```\n\n### Speech Synthesis\n1. imports:\n```js\nconst { RemoteSpeechModel, Text2SpeechInput } = require('intellinode');\n```\n2. call google model:\n```js\nconst speechModel = new RemoteSpeechModel('google-key', 'google');\nconst audioContent = await speechModel.generateSpeech(new Text2SpeechInput({\n  text: text,\n  language: 'en-gb'\n}));\n```\n3. or Gemini speech (base64 WAV): `new RemoteSpeechModel(geminiKey, 'gemini')`, or `'vertex'` with an Agent Platform key.\n### Hugging Face Inference\n1. imports:\n```js\nconst { HuggingWrapper } =  require('intellinode');\n```\n2. call any model id\n```js\nconst inference = new HuggingWrapper('HF-key');\nconst result = await huggingWrapper.generateText(\n   modelId='facebook/bart-large-cnn',\n   data={ inputs: 'The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building...' });\n```\nThe available hugging-face functions: `generateText`, `generateImage`, `processImage`.\n\nCheck the [samples](https://github.com/Barqawiz/IntelliNode/tree/main/samples/command_sample) for more code details including automating your daily tasks using AI.\n\n## Utilities\n### Prompt Engineering\nGenerate improved prompts using LLMs:\n```js\nconst promptTemp = await Prompt.fromChatGPT(\"fantasy image with ninja jumping across buildings\", openaiApiKey);\nconsole.log(promptTemp.getInput());\n```\n\n### Azure Openai Access\nTo access Openai services from your Azure account, you have to call the following function at the beginning of your application:\n```js\nconst { ProxyHelper } = require('intellinode');\nProxyHelper.getInstance().setAzureOpenai(resourceName);\n```\n### Custom proxy\nCheck the code to access the chatbot through a proxy: [proxy chatbot](https://github.com/Barqawiz/IntelliNode/blob/main/samples/command_sample/test_chatbot_proxy.js).\n\n### Model Context Protocol (MCP)\nUse every provider from Claude Code, Codex, Cursor or VS Code: the `intellinode` command starts an MCP server with cross-provider tools (ask a model, consensus, web search with sources, code review, fixes, tests, components, SQL, OpenAPI, mock data, images, speech):\n```bash\nclaude mcp add intellinode -e OPENAI_API_KEY=sk-... -e ANTHROPIC_API_KEY=sk-ant-... -- npx -y intellinode mcp\ncodex mcp add intellinode --env GEMINI_API_KEY=... -- npx -y intellinode mcp\n```\nOr give your chatbot the tools of any MCP server (stdio or HTTP):\n```js\nconst files = new MCPClient({ command: 'npx', args: ['-y', '@modelcontextprotocol/server-filesystem', process.cwd()] });\nconst { text } = await new Chatbot(openaiKey).runTools(input, files);   // MCP tools run through the tool loop\n```\nThe setup for Cursor and VS Code, the tool list, the HTTP mode and building your own `MCPServer` are in [MCP_IMPLEMENTATION.md](MCP_IMPLEMENTATION.md).\nTypeScript users get full typings from the bundled `index.d.ts`.\n\n### Agent skill for Claude Code and Codex\nThe package ships an agent skill that teaches Claude Code and Codex how to build apps with IntelliNode (assistants, RAG, vector stores, Gemini and Vertex AI, MCP), with a ready chat app template:\n```bash\nnpx intellinode skill install            # ~/.claude/skills and ~/.agents/skills\nnpx intellinode skill install --project  # .claude/skills and .agents/skills in this repository\n```\nOr install the plugin, which also adds the MCP server: `claude plugin marketplace add intelligentnode/IntelliNode`, then `claude plugin install intellinode@intellinode-js` (Codex: `codex plugin marketplace add intelligentnode/IntelliNode`).\n\n### Coding Agent\nPoint the agent at a repository and give it a task: it reads, edits, searches and runs commands (all confined to the workspace) and keeps iterating until the test command passes, on any chat provider:\n```js\nconst { CodingAgent } = require('intellinode');\nconst agent = new CodingAgent({ apiKey: anthropicKey, provider: 'anthropic', workspace: './my_repo' });\nconst result = await agent.run('Fix the failing tests in calc.js', { testCommand: 'npm test' });\nconsole.log(result.success, result.summary);   // result.iterations, result.testOutput\n```\n\n# :closed_book: Documentation\n- [IntelliNode Docs](https://doc.intellinode.ai/docs/npm): Detailed documentation about IntelliNode.\n- [Showcase](https://show.intellinode.ai/): Explore interactive demonstrations of IntelliNode's capabilities.\n- [Samples](https://github.com/Barqawiz/IntelliNode/tree/main/samples/command_sample): Get started with IntelliNode using well-documented code samples.\n- [Model Evaluation](https://doc.intellinode.ai/docs/npm/functions/llm-evaluation): A swift approach to compare the performance of multiple large langiage models like gpt4, gemini, llama and cohere.\n- [LLM as Microservice](https://www.kdnuggets.com/building-microservice-for-multichat-backends-using-llama-and-chatgpt): For scalable production.\n- [Fine-tuning Tutorial](https://doc.intellinode.ai/docs/npm/controllers/fine-tuning): Learn how to tune LLMs with yout data.\n- [Chatbot With Your Docs](https://doc.intellinode.ai/docs/npm/chatbot/docs-chat): Tutorial to augment any LLM provider with your docs and images.\n- [Frontend connector](https://www.intellinode.ai/docs/npm/frontend): Connect directly to models from your browser without the need for server-side integration.\n\n# Pillars\n- **The wrapper layer** provides low-level access to the latest AI models\n- **The controller layer** offers a unified input to any AI model by handling the differences. So you can switch between models like Openai and Cohere without changing the code.\n- **The function layer** provides abstract functionality that extends based on the app's use cases. For example, an easy-to-use chatbot or marketing content generation utilities.\n\nIntelliNode includes vector store adapters (Google Cloud, Pinecone, Qdrant, Chroma, Weaviate, Milvus, Elasticsearch, pgvector, MongoDB Atlas) and works next to libraries like langchain.\n\n# License\nApache License\n\nCopyright 2023 IntelliNode\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.","readmeFilename":"README.md"}