diff --git a/langGraph.js/.gitignore b/langGraph.js/.gitignore new file mode 100644 index 0000000..3ec544c --- /dev/null +++ b/langGraph.js/.gitignore @@ -0,0 +1,2 @@ +node_modules/ +.env \ No newline at end of file diff --git a/langGraph.js/Agent-1/.env.example b/langGraph.js/Agent-1/.env.example new file mode 100644 index 0000000..61666aa --- /dev/null +++ b/langGraph.js/Agent-1/.env.example @@ -0,0 +1 @@ +GROQ_API_KEY= # "your_groq_api_key" \ No newline at end of file diff --git a/langGraph.js/Agent-1/README.md b/langGraph.js/Agent-1/README.md new file mode 100644 index 0000000..675514a --- /dev/null +++ b/langGraph.js/Agent-1/README.md @@ -0,0 +1,49 @@ +# Minimal AI Chat Agent + +This project showcases a lightweight “Hello World”–style AI assistant built with [LangGraph](https://github.com/langchain-ai/langgraphjs), [LangChain.js](https://js.langchain.com/), and the [Groq](https://groq.com/) language model (`llama-3.3-70b-versatile`). It accepts user input through the terminal and returns concise, accurate responses generated by the LLM. + +This project provides an excellent starting point for experimenting with graph-based AI logic, custom chat flows, or Groq-powered inference. + +## Setup Instructions + +### 1. Install dependencies +```bash +npm install +``` + +### 2. Create a .env file +Create a ```.env``` file in the root directory (or a copy of .env.example ) with your Groq API key: + +```ini +GROQ_API_KEY="your_groq_api_key_here" +``` +You can get a free API key from [Groq](https://console.groq.com). + +### 3. Run the Assistant +```bash +node index.js +``` +(Make sure you're using Node.js version 18 or above) + +## Example Usage +Once started, you can chat with the assistant in your terminal: + +``` +User: What are the principles of OOPS? +AI: (whatever answer we get from LLM) +``` + + +## Graph Structure + +Here is the visual representation of the LangGraph workflow. + +![Graph Structure](./assets/graph.png) + +## Results + +Here is a sample output from the terminal after interacting with the assistant: + +![AI Result Screenshot](./assets/result1.png) + +![AI Result Screenshot](./assets/result2.png) diff --git a/langGraph.js/Agent-1/assets/graph.png b/langGraph.js/Agent-1/assets/graph.png new file mode 100644 index 0000000..87d4c21 Binary files /dev/null and b/langGraph.js/Agent-1/assets/graph.png differ diff --git a/langGraph.js/Agent-1/assets/result1.png b/langGraph.js/Agent-1/assets/result1.png new file mode 100644 index 0000000..ace6529 Binary files /dev/null and b/langGraph.js/Agent-1/assets/result1.png differ diff --git a/langGraph.js/Agent-1/assets/result2.png b/langGraph.js/Agent-1/assets/result2.png new file mode 100644 index 0000000..9bb02f9 Binary files /dev/null and b/langGraph.js/Agent-1/assets/result2.png differ diff --git a/langGraph.js/Agent-1/index.js b/langGraph.js/Agent-1/index.js new file mode 100644 index 0000000..e905fda --- /dev/null +++ b/langGraph.js/Agent-1/index.js @@ -0,0 +1,54 @@ +import { config } from 'dotenv' // Load environment variables from .env file +config() + +// Import prompt-sync for user input in terminal +import promptSync from 'prompt-sync'; +const prompt = promptSync(); + +// Import necessary LangChain modules +import { ChatGroq } from "@langchain/groq"; +import { Graph} from '@langchain/langgraph' + +// Initialize the ChatGroq model with API key and parameters +const model = new ChatGroq({ + apiKey: process.env.GROQ_API_KEY, // API key stored in .env + model: 'llama-3.3-70b-versatile', // Selected Groq model + temperature: 0.1 // Low temperature for focused responses +}) + +// Create a new LangGraph workflow +const graph = new Graph() + +// Add a node named "chat" that handles LLM response generation +graph.addNode('chat', async (input) => { + // Invoke the model with the incoming user message + const res = await model.invoke([{ role: "user", content: input }]); + return res.content ; // Return only the generated text +}); + +// Link start → chat → end in the graph +graph.addEdge("__start__", "chat"); +graph.addEdge("chat" ,"__end__") + +// Compile the graph for execution +const compiledGraph = graph.compile(); + +// Function to interact with the agent +const agentCall = async () => { + try { + + // Prompt user for input + let userInput = prompt("Hi user! I am you AI Assistant. Enter your query: "); + + // Invoke the graph with a system-style instruction + user query + const result = await compiledGraph.invoke("You are a helpful AI assistant. Provide a detailed yet concise response to user query in about 50 - 100 words with accuracy. The user query is: " + userInput); + console.log("Result: ", result); // Output the final model response + } + catch (err) { + // Catch and display errors from graph execution or API calls + console.error("Error running graph: ", err); + } +}; + +// Start the agent interaction +agentCall(); \ No newline at end of file diff --git a/langGraph.js/Agent-1/package-lock.json b/langGraph.js/Agent-1/package-lock.json new file mode 100644 index 0000000..f77c57b --- /dev/null +++ b/langGraph.js/Agent-1/package-lock.json @@ -0,0 +1,2821 @@ +{ + "name": "my-langgraph-app", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "my-langgraph-app", + "version": "1.0.0", + "license": "ISC", + "dependencies": { + "@langchain/community": "^0.3.47", + "@langchain/core": "^0.3.61", + "@langchain/groq": "^0.2.3", + "@langchain/langgraph": "^0.3.4", + "dotenv": "^16.5.0", + "langchain": "^0.3.29", + "prompt-sync": "^4.2.0" + } + }, + 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\"Error: no test specified\" && exit 1" + }, + "keywords": [], + "author": "", + "license": "ISC", + "description": "", + "dependencies": { + "@langchain/community": "^0.3.47", + "@langchain/core": "^0.3.61", + "@langchain/groq": "^0.2.3", + "@langchain/langgraph": "^0.3.4", + "dotenv": "^16.5.0", + "langchain": "^0.3.29", + "prompt-sync": "^4.2.0" + }, + "type": "module" +} diff --git a/langGraph.js/Agent-2/.env.example b/langGraph.js/Agent-2/.env.example new file mode 100644 index 0000000..61666aa --- /dev/null +++ b/langGraph.js/Agent-2/.env.example @@ -0,0 +1 @@ +GROQ_API_KEY= # "your_groq_api_key" \ No newline at end of file diff --git a/langGraph.js/Agent-2/README.md b/langGraph.js/Agent-2/README.md new file mode 100644 index 0000000..f89e8fa --- /dev/null +++ b/langGraph.js/Agent-2/README.md @@ -0,0 +1,80 @@ +# Conversational AI Agent + +This project is a simple conversational AI assistant built using: + +- [LangGraph](https://github.com/langchain-ai/langgraphjs) for managing conversational flow using graphs + +- [LangChain](https://js.langchain.com/) for LLM integration and memory abstraction + +- [Groq](https://console.groq.com/) for ultra-fast inference with LLaMA 3 models + +- [BufferMemory](https://v03.api.js.langchain.com/classes/langchain.memory.BufferMemory.html)– to persist conversation history across user turns + +It’s a step up from Agent 1, introducing Conversational Memory, which lets the agent remember previous questions and answers to provide context-aware responses. With BufferMemory for maintaining dialogue history, this project is an excellent starting point for anyone exploring how to build responsive, memory-enabled AI chat systems. +## Setup Instructions + +### 1. Install dependencies + +```bash +npm install +``` + +### 2. Create ```.env``` file +Create a ```.env``` file in the root directory with your Groq API key: + +```ini +GROQ_API_KEY=your-groq-api-key-here +``` +You can get your API key from: https://console.groq.com + +### 3. Running the Assistant + +```bash +node index.js +``` +Then chat with the assistant. Type exit to quit the conversation. + +## Graph Structure + +Here is the visual representation of the LangGraph workflow. + +![Graph Structure](./assets/graph.png) + +## How Memory Works (Step-by-Step) + +We use ```BufferMemory``` to keep track of the entire conversation so the AI assistant can generate context-aware responses. Here’s how it works: + +Create a ```BufferMemory``` instance +- Load memory → get history +- Build new message list: ```SystemMessage``` + history + ```HumanMessage``` +- Call the LLM +- Save the interaction (input, output) into memory + + This is repeated for every message you send + + +## Usage + +Once started, you can chat with the assistant in your terminal: + +``` +User: My name is Sakhi, and I am an engineering student interested in compiler design and ML. What are some good career options? +AI: (some answer from LLM) +User: What are some relevant textbooks that align with my interests? +AI: (based on prior info.... ) +``` + +To exit: +``` +You: exit +``` + +## Example Result + +Here is a sample output from the terminal after interacting with the assistant: + +![AI Result Screenshot](./assets/result1.png) + +![AI Result Screenshot](./assets/result2.png) + +The agent remembers the previous state/chat while responding to current query. \ No newline at end of file diff --git a/langGraph.js/Agent-2/assets/graph.png b/langGraph.js/Agent-2/assets/graph.png new file mode 100644 index 0000000..87d4c21 Binary files /dev/null and b/langGraph.js/Agent-2/assets/graph.png differ diff --git a/langGraph.js/Agent-2/assets/result1.png b/langGraph.js/Agent-2/assets/result1.png new file mode 100644 index 0000000..b63206a Binary files /dev/null and b/langGraph.js/Agent-2/assets/result1.png differ diff --git a/langGraph.js/Agent-2/assets/result2.png b/langGraph.js/Agent-2/assets/result2.png new file mode 100644 index 0000000..75b1bc4 Binary files /dev/null and b/langGraph.js/Agent-2/assets/result2.png differ diff --git a/langGraph.js/Agent-2/index.js b/langGraph.js/Agent-2/index.js new file mode 100644 index 0000000..6702d60 --- /dev/null +++ b/langGraph.js/Agent-2/index.js @@ -0,0 +1,81 @@ +import { config } from 'dotenv'; +config(); + +import promptSync from 'prompt-sync'; +const prompt = promptSync(); + +import { ChatGroq } from "@langchain/groq"; +import { Graph } from "@langchain/langgraph"; +import { BufferMemory } from "langchain/memory"; +import { HumanMessage, SystemMessage } from "@langchain/core/messages"; + +// Initialize memory to store conversation history +const memory = new BufferMemory({ + returnMessages: true, + memoryKey: "history" // key used to extract from memory.loadMemoryVariables() +}); + +// Initialize the Groq chat model +const model = new ChatGroq({ + apiKey: process.env.GROQ_API_KEY, + model: 'meta-llama/llama-4-maverick-17b-128e-instruct', + temperature: 0.2 +}); + +const graph = new Graph(); + +// Define a "chat" node that processes conversation +graph.addNode("chat", async ({ input, history }) => { + const messages = [ // Base system instruction for the AI + new SystemMessage("You are a helpful AI assistant. Keep answers concise and friendly, within 50-100 words.") + ]; + + if (history && history.length > 0) { + messages.push(...history); // pulled from BufferMemory + } + + // Add new human message + messages.push(new HumanMessage(input)); + + const response = await model.invoke(messages); + + // Save user input + model output in memory for future context + await memory.saveContext({ input }, { output: response.content }); + + return response.content; +}); + +// Define graph edges +graph.addEdge("__start__", "chat"); +graph.addEdge("chat", "__end__"); + +const compiledGraph = graph.compile(); + +// Function to interact with the agent +const agentCall = async () => { + try { + console.log("Hi user! I am your AI Assistant. Let's chat."); + while (true) { + const userInput = prompt("You: "); + if (userInput.toLowerCase() === "exit") break; + + // Load chat history stored by BufferMemory + const memoryVars = await memory.loadMemoryVariables({}); // pulls history + // returns an object like { history: [HumanMessage, AIMessage, HumanMessage, ...] } + + const result = await compiledGraph.invoke({ + input: userInput, + history: memoryVars.history ?? 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"module", + "main": "index.js", + "scripts": { + "test": "echo \"Error: no test specified\" && exit 1" + }, + "dependencies": { + "@langchain/community": "^0.3.47", + "@langchain/core": "^0.3.61", + "@langchain/groq": "^0.2.3", + "@langchain/langgraph": "^0.3.5", + "dotenv": "^16.5.0", + "prompt-sync": "^4.2.0" + } +} diff --git a/langGraph.js/Agent-3/.env.example b/langGraph.js/Agent-3/.env.example new file mode 100644 index 0000000..c27dd99 --- /dev/null +++ b/langGraph.js/Agent-3/.env.example @@ -0,0 +1,2 @@ +GROQ_API_KEY= # "your_groq_api_key" +TAVILY_API_KEY= # "your_tavil_api_key" \ No newline at end of file diff --git a/langGraph.js/Agent-3/README.md b/langGraph.js/Agent-3/README.md new file mode 100644 index 0000000..c9d320e --- /dev/null +++ b/langGraph.js/Agent-3/README.md @@ -0,0 +1,77 @@ +# AI Assistant with Real-Time Web Search + +This project is a **web-enabled AI conversational assistant** built with [LangGraph](https://langchain-ai.github.io/langgraphjs/), [Groq](https://groq.com), and [Tavily](https://tavily.com). It supports **real-time web search** via Tavily, **conversation history** with LangChain’s `ChatMessageHistory`, and **summarization** of search results. + +Perfect for beginners, this assistant demonstrates how AI can integrate real-time data and memory to answer user queries intelligently. + +## About Tavily +**Tavily** is a web search tool that indexes and retrieves up-to-date information from the internet. It works by scanning multiple sources in real time, ranking results for relevance, and returning structured content that can be easily processed by applications or AI systems. Tavily essentially acts as a bridge between live web data and AI models, providing fast access to current facts, news, and other web content. + +## Features +- Uses the **Tavily Search Tool** for queries requiring **current or real-time information** +- Handles general knowledge, greetings, and follow-ups naturally +- **LLM reasoning** powered by Groq’s `meta-llama/llama-4-maverick-17b-128e-instruct` +- **Session memory** with `ChatMessageHistory` for context-aware responses +- **Summarization** of search results for concise output +- Supports **continuous interaction**, allowing multi-turn conversation until the user exits +- Input validation with **Zod** – ensures user queries are correctly formatted and reliable before processing + +## Graph Structure + +Here is the visual representation of the LangGraph workflow. + +![Graph Structure](./assets/graph.png) + +## Getting Started + +### 1. Install dependencies + +```bash +npm install +``` + +### 2. Set up environment variables +Create ```.env``` file in root directory: +Add your API keys inside it. + +```bash +GROQ_API_KEY=your_groq_api_key_here +TAVILY_API_KEY=your_tavily_api_key_here +``` + +Groq: Get your API key from [Groq Console](https://console.groq.com/home).\ +Tavily: Get your API key from [Tavily Dashboard](https://app.tavily.com/home). + +### 3. Running the Assistant + +```bash +node index.js +``` +Then chat with your assistant. Type exit to quit the conversation. + +## Usage + +Once started, you can chat with the assistant in your terminal: + +``` +Hi user! I am your AI Assistant. Let's chat. +You: What is the capital of Japan? +Agent: The capital of Japan is Tokyo. + +You: What’s the latest news about AI chips? +Agent: [Summarized real-time info...] +``` + +To exit: +``` +You: exit +``` + +## Example Result + +Here is a sample output from the terminal after interacting with the assistant: + +![AI Result Screenshot](./assets/image.png) + +## **Demo** +Check out a demo video of the project [here](https://github.com/user-attachments/assets/caf41d65-56b2-4cf3-a3fd-9246a84ac1f5) \ No newline at end of file diff --git a/langGraph.js/Agent-3/assets/graph.png b/langGraph.js/Agent-3/assets/graph.png new file mode 100644 index 0000000..5c93ef1 Binary files /dev/null and b/langGraph.js/Agent-3/assets/graph.png differ diff --git a/langGraph.js/Agent-3/assets/image.png b/langGraph.js/Agent-3/assets/image.png new file mode 100644 index 0000000..bce1fd8 Binary files /dev/null and b/langGraph.js/Agent-3/assets/image.png differ diff --git a/langGraph.js/Agent-3/index.js b/langGraph.js/Agent-3/index.js new file mode 100644 index 0000000..2f01ff5 --- /dev/null +++ b/langGraph.js/Agent-3/index.js @@ -0,0 +1,191 @@ +import promptSync from 'prompt-sync'; +const prompt = promptSync(); +import dotenv from "dotenv"; +dotenv.config(); + +import { StateGraph } from "@langchain/langgraph"; +import { ChatGroq } from "@langchain/groq"; +import { TavilySearch } from "@langchain/tavily"; +import { ChatMessageHistory } from "langchain/stores/message/in_memory"; +import { AIMessage, HumanMessage } from "@langchain/core/messages"; +import { z } from "zod"; +import { ToolNode } from "@langchain/langgraph/prebuilt"; +import { tool } from "@langchain/core/tools"; + +// === SCHEMA === +// Define the schema for the graph's state +const schema = z.object({ + input: z.string(), + finalAnswer: z.string().optional(), + messages: z.array(z.any()).optional(), // Required for ToolNode passes tool calls/results +}); + +// Initialize Tavily Search with your API key +const tavilyTool = new TavilySearch({ apiKey: process.env.TAVILY_API_KEY }); + +// Groq LLM +const llm = new ChatGroq({ + apiKey: process.env.GROQ_API_KEY, + model: "meta-llama/llama-4-maverick-17b-128e-instruct", + temperature: 0.1, // Low temperature = more deterministic answers +}); + +// Define a tool wrapper for Tavily Search +// This is what the LLM will call when it decides tool use is necessary +const searchTool = tool( + async ({ query }) => { + const toolResult = await tavilyTool.invoke({ query }); + return { + results: toolResult?.results || [], + }; + }, + { + name: "search", + description: + "Search the web for current or real-time information, such as today's date, time, latest news, or recent events.", + schema: z.object({ + query: z.string().describe("The search query to send to the web"), + }), + } +); + +// Wrap tools into a ToolNode (LangGraph requirement) +const toolNode = new ToolNode([searchTool]) + +// LLM with tools bound +const llmWithTools = llm.bindTools([searchTool]); + +// In-memory chat history +const messageHistory = new ChatMessageHistory(); + +// === LLM Node === +// decides whether to: answer directly or call a tool, depending on the user's question. +async function llmNode(state) { + const query = state.input; + + const history = await messageHistory.getMessages(); // Retrieve conversation history + const chat = [ + { + role: "system", + content: `You are an assistant with access to a tool called "search" for finding real-time or current information, like today's date or live events. +Only use the tool if the user's query requires current or web-based information. For greetings or general knowledge, reply directly without calling any tool. +⚠️ If the input includes a "[Summarized answer:" section, it means the tool has already been called and the result was summarized. +Carefully read the full input, and call the tool again only and only if the summarised answer is not sufficient to answer the query. Otherwise, respond directly and do not call the tool again.`, + }, + ...history, + new HumanMessage(query), + ]; + + const response = await llmWithTools.invoke(chat); + + let messages; + // If LLM requested a tool call (response.tool_calls exists) + if ("tool_calls" in response && response.tool_calls?.length) { + messages = [new AIMessage({ tool_calls: response.tool_calls, content: "" })]; + } else { + // No tool call → Give direct answer + const finalAnswer = response.content || "Sorry, I didn't understand."; + + // Save this turn in memory + await messageHistory.addMessage(new HumanMessage(query)); + await messageHistory.addMessage(new AIMessage(finalAnswer)); + return { finalAnswer }; + } + + // Save tool call request into memory + await messageHistory.addMessage(new HumanMessage(query)); + await messageHistory.addMessage(messages[0]); + + return { + messages, + input: state.input, + }; +} + +// === Summarization Node === +// This node receives the raw output from the tool and summarizes it +async function summarizeNode(state) { + const toolMessage = state.messages[state.messages.length - 1]; + let toolResult = toolMessage?.tool_call_result; + + // Some tool responses may come as JSON strings → parse them + if (!toolResult && typeof toolMessage?.content === "string") { + try { + toolResult = JSON.parse(toolMessage.content); + } catch (err) { + console.error("Failed to parse tool content as JSON:", toolMessage.content); + } +} + + // Extract top 3 results for summarization + const combinedText = toolResult.results + ?.slice(0, 3) + .map((r) => r.content) + .filter(Boolean) + .join("\n\n"); + + if (!combinedText) { + return { finalAnswer: "Sorry, I couldn't find relevant information." }; + } + + // Summarize the extracted text using the LLM + const summaryPrompt = [ + { + role: "system", + content: + "Summarize the following information clearly and concisely for the user. Don't list sources or include links.", + }, + { + role: "user", + content: combinedText, + }, + ]; + + const summaryResponse = await llm.invoke(summaryPrompt); + + const finalAnswer = summaryResponse?.content || "Sorry, I couldn't generate a summary."; + + await messageHistory.addMessage(new AIMessage(finalAnswer)); + + return {finalAnswer, + input: `${state.input}\n[Summarized answer: ${finalAnswer}]`, }; +} + +// === Router Logic === +// Decides which node to route to after LLM execution +function routerFromLLM(state) { + + // If tool call exists → go to "tool" node + if (state.messages?.[0]?.tool_calls?.length) return "tool"; + + return "__end__"; // // Otherwise → finish execution +} + +// === Build Graph === +const graph = new StateGraph(schema); +const compiledGraph = graph + .addNode("llm", llmNode) + .addNode("tool", toolNode) + .addNode("summarize", summarizeNode) + .addEdge("__start__", "llm") + .addConditionalEdges("llm", routerFromLLM) + .addEdge("tool", "summarize") + .addEdge("summarize", "llm") + .compile(); + +// === CHAT LOOP === +const agentCall = async () => { + try { + console.log("Hi user! I am your AI Assistant. Let's chat."); + while (true) { + const userinput = prompt("You: "); + if (userinput.toLowerCase() === "exit") break; + const result = await compiledGraph.invoke({ input: userinput }); + console.log("Agent:", result.finalAnswer, "\n"); + } + } catch (err) { + console.error("Error running graph:", err); + } +}; + +agentCall(); diff --git a/langGraph.js/Agent-3/package-lock.json b/langGraph.js/Agent-3/package-lock.json new file mode 100644 index 0000000..527ce54 --- /dev/null +++ b/langGraph.js/Agent-3/package-lock.json @@ -0,0 +1,2879 @@ +{ + "name": "agent-3", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "agent-3", + "version": "1.0.0", + "license": "ISC", + "dependencies": { + "@langchain/community": "^0.3.47", + "@langchain/core": "^0.3.61", + "@langchain/groq": "^0.2.3", + "@langchain/langgraph": "^0.3.5", + "@langchain/tavily": "^0.1.3", + "dotenv": "^16.6.1", + "prompt-sync": "^4.2.0", + "zod": "^3.25.67" + } + }, + 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"prompt-sync": "^4.2.0", + "zod": "^3.25.67" + } +} diff --git a/langGraph.js/Agent-4/.env.example b/langGraph.js/Agent-4/.env.example new file mode 100644 index 0000000..f2f9c95 --- /dev/null +++ b/langGraph.js/Agent-4/.env.example @@ -0,0 +1 @@ +GROQ_API_KEY= # "your_groq_api_key" diff --git a/langGraph.js/Agent-4/.gitignore b/langGraph.js/Agent-4/.gitignore new file mode 100644 index 0000000..def1ba8 --- /dev/null +++ b/langGraph.js/Agent-4/.gitignore @@ -0,0 +1,4 @@ +node_modules +.env +eng.traineddata +resume_sample.png diff --git a/langGraph.js/Agent-4/README.md b/langGraph.js/Agent-4/README.md new file mode 100644 index 0000000..63c7da7 --- /dev/null +++ b/langGraph.js/Agent-4/README.md @@ -0,0 +1,91 @@ +# AI Agent for Document-Based Question Answering + +This project is an AI-driven document question-answering agent that uses **Optical Character Recognition (OCR)** to extract text from images, stores the content in a **vector database (ChromaDB)**, and ask **natural language queries** about its content using a **large language model (Groq Llama 4 Maverick series model)**. + +## Features +- **RAG Pipeline**: Implements a Retrieval-Augmented Generation workflow. +- **OCR Integration**: Extracts text from any scanned or photographed image-based document using [Tesseract.js](https://tesseract.projectnaptha.com/), enabling support for resumes, forms, receipts, notes, certificates, reference material and more. +- **Vectorization & Storage**: Creates embeddings with [Hugging Face sentence-transformers](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) and stores them in a [Chroma](https://www.trychroma.com/) vector store. +- **Fast LLM Inference**: Powered by [Groq's meta-llama/llama-4-maverick-17b-128e-instruct](https://console.groq.com/docs/model/meta-llama/llama-4-maverick-17b-128e-instruct) for quick, intelligent responses. +- **Conversational Q&A**: Allows you to ask multiple questions about the image document in a single session. + +## Getting Started +### 1. Prerequisites +Before starting, ensure you have the following installed: +- [Node.js (v18+)](https://nodejs.org/) +- [Docker Desktop](https://www.docker.com/products/docker-desktop) + +> **Important:** +> Before running any Docker command, make sure **Docker Desktop is open and running**. +--- + +### 2. Install dependencies +```bash +npm install +``` +--- + +### 3. Environment Variables +Create a ```.env``` file in the root folder and add your Groq API key: +```bash +GROQ_API_KEY=your_groq_api_key_here +``` +Get your API key here: https://console.groq.com/keys + +### 4. Running Chroma Database +This project uses ChromaDB, a lightweight open-source vector database. +To run it locally using Docker: +```bash +# Step 1: Make sure Docker Desktop is open +# Step 2: Pull Chroma image +docker pull chromadb/chroma +``` +```bash +# Step 3: Run Chroma container +docker run -p 8000:8000 chromadb/chroma +``` +Reference: [ChromaDB integration with LangChain](https://js.langchain.com/docs/integrations/vectorstores/chroma/) + +### 5. Running the Project +In a separate terminal, start the Node.js app: +```bash +node index.js +``` +You’ll be prompted to enter file path: +```bash +Enter file path: resume_sample.png +``` +Then you can start asking questions interactively, like: +```bash +Ask a question (or 'exit'): What is the candidate's name? +Answer: (based on the resume - the ans should be provided) +``` +## Libraries and Tools Used +| Library / Tool | Purpose | Official Reference | +|----------------|----------|--------------------| +| **@langchain/langgraph** | Enables graph-based workflows in LangChain. It connects logical components like OCR, vectorization, and Q&A nodes, and can generate a visual graph (graph.png) of the pipeline. | [LangGraph Docs](https://js.langchain.com/docs/langgraph) | +| **@langchain/groq** | Integrates **Groq’s high-speed LLMs** (like Meta Llama 4 Maverick 17B) with LangChain to perform question answering and reasoning. | [LangChain Groq Integration](https://js.langchain.com/docs/integrations/chat/groq/) | +| **@langchain/community/vectorstores/chroma** | Provides integration between LangChain and **ChromaDB**, allowing storage and retrieval of document embeddings for semantic search. | [Chroma Vector Store Integration](https://js.langchain.com/docs/integrations/vectorstores/chroma) | +| **@langchain/community/embeddings/huggingface_transformers** | Generates vector embeddings for text using **Hugging Face Transformers**, specifically the all-MiniLM-L6-v2 sentence transformer model. | [Hugging Face Model Page](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | +| **langchain/stores/message/in_memory** | Handles **in-memory chat history** for LangChain conversations, maintaining dialogue context between the user and the LLM. | [LangChain Memory Docs](https://v03.api.js.langchain.com/modules/_langchain_community.stores_message_in_memory.html)| +| **tesseract.js** | Performs **Optical Character Recognition (OCR)** on image-based documents, extracting text for further processing. | [Tesseract.js GitHub](https://github.com/naptha/tesseract.js) | +| **dotenv** | Loads environment variables (like API keys) securely from a .env file into process.env. | [dotenv npm](https://www.npmjs.com/package/dotenv) | +| **Docker Desktop** | Required to run **ChromaDB** locally inside a container. It must be started before running any Docker commands. | [Docker Desktop](https://www.docker.com/products/docker-desktop) | +| **ChromaDB** | An open-source **vector database** used to store and retrieve text embeddings for RAG pipelines. Runs locally via Docker. | [ChromaDB Docs](https://docs.trychroma.com/) | +| **Groq LLM API** | Provides access to **Groq’s ultra-fast inference servers** for Llama-based large language models. Used here for natural language Q&A. | [Groq API Console](https://console.groq.com/) | +--- +## Workflow Summary +Here is the visual representation of the LangGraph workflow. + +![Graph Structure](./assets/graph.png) + +- User Input → provide document path. +- OCRNode → extracts text using tesseract.js. +- VectorizeNode → splits text into chunks, embeds using HuggingFace, and stores in Chroma. +- QANode → retrieves relevant chunks via similarity search and answers questions using Groq LLM. +- Chat History → conversation maintained across queries. + +## Example Result +Here is a sample output from the terminal after interacting with the assistant: + +![AI Result Screenshot](./assets/sample_result.png) \ No newline at end of file diff --git a/langGraph.js/Agent-4/assets/graph.png b/langGraph.js/Agent-4/assets/graph.png new file mode 100644 index 0000000..a75f54e Binary files /dev/null and b/langGraph.js/Agent-4/assets/graph.png differ diff --git a/langGraph.js/Agent-4/assets/sample_result.png b/langGraph.js/Agent-4/assets/sample_result.png new file mode 100644 index 0000000..b097dc7 Binary files /dev/null and b/langGraph.js/Agent-4/assets/sample_result.png differ diff --git a/langGraph.js/Agent-4/index.js b/langGraph.js/Agent-4/index.js new file mode 100644 index 0000000..8c0acdd --- /dev/null +++ b/langGraph.js/Agent-4/index.js @@ -0,0 +1,174 @@ +import { Graph } from "@langchain/langgraph"; +import { ChatGroq } from "@langchain/groq"; + +import { Chroma } from "@langchain/community/vectorstores/chroma"; +import { HuggingFaceTransformersEmbeddings } from "@langchain/community/embeddings/huggingface_transformers"; + +import { ChatMessageHistory } from "langchain/stores/message/in_memory"; +import { SystemMessage } from "@langchain/core/messages"; + +import Tesseract from "tesseract.js"; // OCR library for extracting text from images + +import { Document } from "@langchain/core/documents"; +import { RecursiveCharacterTextSplitter } from "langchain/text_splitter"; + +import { RunnableSequence } from "@langchain/core/runnables"; +import { StringOutputParser } from "@langchain/core/output_parsers"; + +import promptSync from "prompt-sync"; +const prompt = promptSync(); + +import dotenv from "dotenv"; +dotenv.config(); + +import { EventEmitter } from "events"; // Prevent event emitter warnings +EventEmitter.defaultMaxListeners = 20; + +const llm = new ChatGroq({ + apiKey: process.env.GROQ_API_KEY, + model: 'meta-llama/llama-4-maverick-17b-128e-instruct', + temperature: 0 +}); + +// Embedding model used for vector store +const embeddings = new HuggingFaceTransformersEmbeddings({ + modelName: "sentence-transformers/all-MiniLM-L6-v2", +}); + +// -------- INITIAL STATE ---------- +const initialState ={ + filePath: null, + userId: null, + text: null, + vectorStore: null, + chatHistory: null, +}; + +// -------------------- OCR NODE -------------------- +// Extracts text content from an image using Tesseract OCR. +async function OCRNode(state) { + const { filePath } = state; + let text = ''; + try { + const results = await Tesseract.recognize( + filePath, // Path to the image file + 'eng', // Language code (e.g., 'eng' for English) + ); + text = results.data.text; + } catch (error) { + console.error("Error during OCR:", error); + } + return {...state, text }; +} + +// -------------------- VECTORIZATION NODE -------------------- +// Splits extracted text into chunks, embeds them, and stores them in a Chroma vector store. +async function VectorizeNode(state) { + const { text, userId } = state; + const collectionName = `docs_${userId}`; + + // Split text into overlapping chunks for better retrieval accuracy + const splitter = new RecursiveCharacterTextSplitter({ + chunkSize: 500, // Max size of each chunk + chunkOverlap: 50 // Overlap between chunks + }); + /* RecursiveCharacterTextSplitter : + The splitter uses a list of separators (e.g., ["\n\n", "\n", " ", ""]) and attempts to split the text using the first separator in the list. + If a resulting chunk is smaller than the specified chunkSize, it's considered a valid chunk. + If a chunk is larger than chunkSize, the splitter recursively applies the next separator in the list to that chunk, trying to break it down further until all chunks are within the size limit. + */ + + const docs = await splitter.splitDocuments([ + new Document({ pageContent: text, metadata: { userId, source: "docs" } }) + ]); + + // Add unique id metadata for each chunk + docs.forEach((doc, i) => { + doc.metadata.id = `doc_user123_part_${i}`; // Attach ID inside metadata or a separate map (Chroma handles metadata) + }); + + // Creates a new Chroma instance from an existing collection in the Chroma database. + const vectorStore = await Chroma.fromExistingCollection(embeddings, { + collectionName, + }); + // Add documents only if collection is empty + const count = await vectorStore.collection.count(); + if (count === 0) { + await vectorStore.addDocuments(docs); + } + + console.log(`Vector store created for user: ${userId}`); + return {...state , vectorStore : vectorStore}; +} + +// -------------------- QA(Question - Answer) NODE -------------------- +// Handles user questions by retrieving relevant context from the vector store and generating answers using the LLM. +async function QANode(state) { + const {vectorStore} = state; + let {chatHistory} = state; + + chatHistory = new ChatMessageHistory(); + await chatHistory.addMessage(new SystemMessage("You are an AI assistant helping users extract information from documents.")); + + // Retrieval + LLM pipeline + const retrievalChain = RunnableSequence.from([ + { + // Look up semantic context from vector store + context: async (input) => { + + // Convert the user's natural-language question into an embedding vector + const queryVec = await embeddings.embedQuery(input.question); + // Perform a similarity search in the vector store using the query embedding and returning the top 2 most relevant chunks + const results = await vectorStore.similaritySearchVectorWithScore([queryVec], 2); + // Extract the text content from the retrieved documents. + const context = results?.[0]?.[0]?.pageContent + "\n" + results?.[1]?.[0]?.pageContent; + return context || "No relevant context found."; + }, + question: (input) => input.question, + }, + + // Format prompt for the LLM + async ({ context, question }) => + ` Answer the question based on the following context and chat history:\n\n${context}\n\nChat history: ${chatHistory.messages.map(m => `${m.role}: ${m.content}`).join('\n')}\n\nQuestion: ${question}\nAnswer:`, + llm, + new StringOutputParser(), + ]); + + // Interactive QA loop + while (true) { + console.log("\n"); + const question = prompt("Ask a question (or 'exit'): "); + if (question.toLowerCase() === "exit") break; + + const result = await retrievalChain.invoke({question}); + + await chatHistory.addUserMessage(question); + await chatHistory.addAIMessage(result); + + console.log("Answer:", result); + } + + return {...state , chatHistory: chatHistory }; +} + +const graph = new Graph(initialState) + .addNode("OCRNode", OCRNode) + .addNode("VectorizeNode", VectorizeNode) + .addNode("QANode", QANode) + .addEdge("__start__", "OCRNode") + .addEdge("OCRNode", "VectorizeNode") + .addEdge("VectorizeNode", "QANode") + .addEdge("QANode", "__end__") + .compile(); + +async function main() { + const filePath = prompt("Enter file path: "); + const userId = "user123"; + + await graph.invoke({ + filePath, + userId + }); +} + +main(); diff --git a/langGraph.js/Agent-4/package-lock.json b/langGraph.js/Agent-4/package-lock.json new file mode 100644 index 0000000..778f0c2 --- /dev/null +++ b/langGraph.js/Agent-4/package-lock.json @@ -0,0 +1,4004 @@ +{ + "name": "agent-4", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "agent-4", + "version": "1.0.0", + "license": "ISC", + 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"index.js", + "scripts": { + "test": "echo \"Error: no test specified\" && exit 1" + }, + "keywords": [], + "author": "", + "license": "ISC", + "description": "", + "type": "module" +} diff --git a/langGraph.js/Agent-5/.env.example b/langGraph.js/Agent-5/.env.example new file mode 100644 index 0000000..f2f9c95 --- /dev/null +++ b/langGraph.js/Agent-5/.env.example @@ -0,0 +1 @@ +GROQ_API_KEY= # "your_groq_api_key" diff --git a/langGraph.js/Agent-5/.gitignore b/langGraph.js/Agent-5/.gitignore new file mode 100644 index 0000000..fa66f9c --- /dev/null +++ b/langGraph.js/Agent-5/.gitignore @@ -0,0 +1,5 @@ +node_modules +.env +gcp-tts.json +mind_map.txt +pdfFile.pdf diff --git a/langGraph.js/Agent-5/GCP_TTS_setup.md b/langGraph.js/Agent-5/GCP_TTS_setup.md new file mode 100644 index 0000000..bef82cd --- /dev/null +++ b/langGraph.js/Agent-5/GCP_TTS_setup.md @@ -0,0 +1,27 @@ +# Google Cloud Text-to-Speech Setup Guide + +1. Create a Google Cloud Project + - Go to [Google Cloud Console](https://console.cloud.google.com) + - Click “Create Project” → give it a name → click “Create”. + +2. Enable the Text-to-Speech API + - Navigate to “APIs & Services” → “Library”. + - Search for “Text-to-Speech API” → Click “Enable”. + +3. Create a Service Account + - Go to “IAM & Admin” → “Service Accounts”. + - Click “Create Service Account”. + - Give it a name → click “Create and Continue”. + +4. Assign Roles to the Service Account + - Assign role: “Project” > “Editor” (or a stricter role if preferred). + - Click “Continue” → “Done”. + +5. Generate and Download Credentials + - Click your new service account → “Keys” tab. + - Add Key → Create New Key → choose JSON. + - Download and rename it to `gcp-tts.json` and place it in your project root. + +This file authenticates the Google TTS client used in podcast generation. + +--- diff --git a/langGraph.js/Agent-5/README.md b/langGraph.js/Agent-5/README.md new file mode 100644 index 0000000..665eb66 --- /dev/null +++ b/langGraph.js/Agent-5/README.md @@ -0,0 +1,135 @@ +# Google NotebookLM mimic + +This agent is a **multi-tool AI agent** that can **ingest, store, and process knowledge** from multiple sources (like PDFs, YouTube videos, web pages and raw text) and then **answer questions or perform higher-level tasks** like summarization, note-taking, mind-map creation, or podcast generation. + +It combines **LangChain + LangGraph + Groq LLMs + ChromaDB + Hugging Face Embeddings + Google TTS** to form an end-to-end **Retrieval-Augmented Generation (RAG)** system with integrated reasoning and tool usage. + + +## Features + +- **Multi-source Ingestion:** Load data from **YouTube**, **PDFs**, **webpages**, or direct **text input**. +- **Vector Store Integration:** Uses **ChromaDB** to store embeddings for efficient semantic retrieval. +- **Intelligent Routing:** Automatically classifies user input as a *resource*, *query*, or *exit* using an LLM planner. +- **Conversational Memory:** Maintains multi-turn chat state per user session. +- **Processing Tools:** + - **Mind Map Generation** (produces structured JSON mindmaps) + - **Note Creation** (concise bullet points) + - **Podcast Generation** (two-voice MP3 audio with Google Cloud Text-To-Speech) + - **Summarization** (summarizes text) +- **RAG-Enhanced Query Answering:** Combines contextual retrieval with LLM reasoning for precise answers. + + +## Project Architecture +```bash +├── tools/ + ├── ingestion_tools.js # Tools for loading resources (YouTube, PDF, webpages, text) + └── processing_tools.js # Tools for processing and generating output (notes, podcasts, mindmap and summary) +├── .env # Stores Groq API key +├── gcp-tts.json # Google Cloud TTS API credentials (required for podcast tool) +├── index.js # Main agent orchestration file (LangGraph workflow) +├── package-lock.json # Auto-generated dependency tree +├── package.json # Project metadata and dependencies +├── GCP_TTS_setup.md # Guide for setting up Google Cloud TTS +└── README.md # Project documentation +``` + + +## Getting Started + +### 1. Prerequisites + +Make sure the following are installed on your system: + +- [Node.js (v18+)](https://nodejs.org/) +- [Docker Desktop](https://www.docker.com/products/docker-desktop) +- (Optional) [Google Cloud SDK](https://cloud.google.com/text-to-speech/docs/reference/libraries) if you plan to use the podcast generation feature. + +> **Important:** +> Before running any Docker command, ensure **Docker Desktop is open and running**, otherwise the container will not start. + +--- + +### 2. Install dependencies + +``` +npm install +``` +--- +### 3. Environment Variables + +Create a ```.env``` file in the root directory and add your Groq API key: +```bash +GROQ_API_KEY=your_groq_api_key_here +``` + +Generate a key from: [https://console.groq.com/keys](https://console.groq.com/keys) + +If using the Podcast Tool, also include your Google Cloud Text-to-Speech credentials file: +For detailed instructions on setting this up, see: +[GCP_TTS_setup.md](./GCP_TTS_setup.md) + +--- +### 4. Running Chroma Database + +This agent uses ChromaDB as its local vector store for knowledge retrieval. +Run it using Docker: + +```bash +# Step 1: Make sure Docker Desktop is open +# Step 2: Pull Chroma image +docker pull chromadb/chroma +``` + +```bash +# Step 3: Run Chroma container +docker run -p 8000:8000 chromadb/chroma +``` + +Reference: [ChromaDB integration with LangChain](https://js.langchain.com/docs/integrations/vectorstores/chroma/) + +--- +### 5. Running the Agent + +Once Chroma is running, open a new terminal and run: +```bash +node index.js +``` + +## Libraries and Tools Used + +| Library / Tool | Purpose | Official Reference | +|----------------|----------|--------------------| +| **@langchain/community/vectorstores/chroma** | Provides integration between LangChain and **ChromaDB**, allowing storage and retrieval of document embeddings for semantic search. | [Chroma Vector Store Integration](https://js.langchain.com/docs/integrations/vectorstores/chroma) | +| **@langchain/community/embeddings/huggingface_transformers** | Generates embeddings using **Hugging Face Transformers**, specifically ```sentence-transformers/all-MiniLM-L6-v2.``` | [Hugging Face Model Page](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | +| **langchain/text_splitter** (`RecursiveCharacterTextSplitter`) | Splits large text into smaller overlapping chunks for efficient embedding and retrieval. | [Text Splitter Docs](https://docs.langchain.com/oss/python/integrations/splitters) | +| **@langchain/core/documents** (`Document`) | Represents structured text data (content + metadata) that can be stored in a vector database. | [Document Class Docs](https://v03.api.js.langchain.com/classes/_langchain_core.documents.Document.html) | +| **@langchain/community/document_loaders** | Loads data from different content sources like YouTube, PDFs, and web pages. | [Document Loaders Docs](https://js.langchain.com/docs/integrations/document_loaders/) | +| **@google-cloud/text-to-speech** | Converts generated podcast scripts into audio MP3s using Google Cloud TTS. | [Google Cloud TTS Docs](https://cloud.google.com/text-to-speech/docs) | +| **zod** | Schema validation for tool arguments and user input. | [Zod Docs](https://zod.dev/) | +| **fs (Node.js File System)** | Reads and writes files such as `mind_map.txt`, `podcast_episode_two_voice.mp3`, and `agent_graph.png`. | [Node.js fs Docs](https://nodejs.org/api/fs.html) | +| **Docker Desktop** | Required to run **ChromaDB** locally inside a container. It must be started before running any Docker commands. | [Docker Desktop](https://www.docker.com/products/docker-desktop) | +| **ChromaDB** | An open-source **vector database** used to store and retrieve text embeddings for RAG pipelines. Runs locally via Docker. | [ChromaDB Docs](https://docs.trychroma.com/) | + +--- +## Workflow Summary + +![Graph Workflow](./assets/agent_graph.png) +> The workflow visualization (`agent_graph.png`) is generated using LangGraph’s internal JavaScript API (getGraphAsync() and drawMermaidPng()). +> You can regenerate it anytime by calling `exportGraphImage(app, "./assets/agent_graph.png")` after compiling your graph. + + +- Planner Node → Classifies input as resource / query / exit +- Ingestion Node → Uses ingestion tools to process PDFs/URLs/text +- Ingestion Tool Node → Splits & stores text into Chroma vector DB +- Response Query Node → Retrieves context and answers or invokes tools +- Processing Tool Node → Summarize / Notes / Mind Map creation / Podcast generation + +## Example Result + +Here is a sample output from the terminal after interacting with the assistant: + +![Result Screenshot1](./assets/sample_result1.png) +![Result Screenshot2](./assets/sample_result2.png) +![Result Screenshot3](./assets/sample_result3.png) + +The agent supports parallel execution, allowing it to ingest multiple resources and run several processing tools simultaneously. \ No newline at end of file diff --git a/langGraph.js/Agent-5/assets/agent_graph.png b/langGraph.js/Agent-5/assets/agent_graph.png new file mode 100644 index 0000000..07bc82d Binary files /dev/null and b/langGraph.js/Agent-5/assets/agent_graph.png differ diff --git a/langGraph.js/Agent-5/assets/podcast_episode_two_voice.mp3 b/langGraph.js/Agent-5/assets/podcast_episode_two_voice.mp3 new file mode 100644 index 0000000..f5d768c Binary files /dev/null and b/langGraph.js/Agent-5/assets/podcast_episode_two_voice.mp3 differ diff --git a/langGraph.js/Agent-5/assets/sample_result1.png b/langGraph.js/Agent-5/assets/sample_result1.png new file mode 100644 index 0000000..7c05c91 Binary files /dev/null and b/langGraph.js/Agent-5/assets/sample_result1.png differ diff --git a/langGraph.js/Agent-5/assets/sample_result2.png b/langGraph.js/Agent-5/assets/sample_result2.png new file mode 100644 index 0000000..d4077a9 Binary files /dev/null and b/langGraph.js/Agent-5/assets/sample_result2.png differ diff --git a/langGraph.js/Agent-5/assets/sample_result3.png b/langGraph.js/Agent-5/assets/sample_result3.png new file mode 100644 index 0000000..325919a Binary files /dev/null and b/langGraph.js/Agent-5/assets/sample_result3.png differ diff --git a/langGraph.js/Agent-5/gcp-tts.example.json b/langGraph.js/Agent-5/gcp-tts.example.json new file mode 100644 index 0000000..3f7a870 --- /dev/null +++ b/langGraph.js/Agent-5/gcp-tts.example.json @@ -0,0 +1,13 @@ +{ + "type": "service_account", + "project_id": "your-project id", + "private_key_id": "your-private-key-id", + "private_key": "-----BEGIN PRIVATE KEY-----\nYOUR PRIVATE KEY\n-----END PRIVATE KEY-----\n", + "client_email": "your-client-email", + "client_id": "your-client-id", + "auth_uri": "https://accounts.google.com/o/oauth2/auth", + "token_uri": "https://oauth2.googleapis.com/token", + "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs", + "client_x509_cert_url": "your-client-x509-cert-url", + "universe_domain": "googleapis.com" +} diff --git a/langGraph.js/Agent-5/index.js b/langGraph.js/Agent-5/index.js new file mode 100644 index 0000000..12bb051 --- /dev/null +++ b/langGraph.js/Agent-5/index.js @@ -0,0 +1,315 @@ +import promptSync from 'prompt-sync'; +const prompt = promptSync(); +import dotenv from "dotenv"; +dotenv.config(); + +import { createTools } from "./tools/processing_tools.js"; +import { createIngestionTools } from "./tools/ingestion_tools.js"; + +import { StateGraph, END } from "@langchain/langgraph"; +import { ChatGroq } from "@langchain/groq"; +import { AIMessage, HumanMessage, SystemMessage, ToolMessage } from "@langchain/core/messages"; +import { ToolNode } from "@langchain/langgraph/prebuilt"; + +import { Chroma } from "@langchain/community/vectorstores/chroma"; +import { HuggingFaceTransformersEmbeddings } from "@langchain/community/embeddings/huggingface_transformers"; +import { Document } from "@langchain/core/documents"; +import { RecursiveCharacterTextSplitter } from "langchain/text_splitter"; + +import fs from "fs"; + + +// 1. STATE DEFINITION + +const agentState = { //Represents the state of our agent. + lastInput: { // The most recent user input. + value: (x, y) => y, // Always replace with the latest input + default: () => "", + }, + messages: { // The history of the conversation. + value: (x, y) => x.concat(y), // Append new messages to existing history + default: () => [], + }, + routeDecision: { // Decision from the planner node to route the graph. + value: (x, y) => y, + default: () => "", + }, + vectorStore: { + value: (x, y) => y, // Always replace with the latest instance + default: () => null, + }, + userId: { // The user ID for the current conversation thread. + value: (x, y) => y, + default: () => "", + } +}; + +// 2. LLM and TOOLS INITIALIZATION + +const llm = new ChatGroq({ + apiKey: process.env.GROQ_API_KEY, + model: "meta-llama/llama-4-maverick-17b-128e-instruct", + temperature: 0, +}); + +// Initialize embeddings model +const embeddings = new HuggingFaceTransformersEmbeddings({ + modelName: "sentence-transformers/all-MiniLM-L6-v2", +}); + +// --- INGESTION TOOLS --- +const ingestionTools = createIngestionTools(); +const llmWithIngestionTools = llm.bindTools(ingestionTools); +// .bind_tools() on an LLM instance, creates a new, modified instance of the LLM with the tool bindings attached, rather than updating the original instance in place. + +// --- PROCESSING TOOLS --- +const processingTools = createTools(llm); +const processingToolNode = new ToolNode(processingTools); +const llmWithProcessingTools = llm.bindTools(processingTools); + +// 3. AGENT NODES + +// The central hub. Classifies the user's input to decide where to go next. +const planner_node = async (state) => { + const { lastInput } = state; + const systemPrompt = `You are a classifier. Your only job is to determine if the user's input is a resource, a query, or an exit command. + - A 'resource' is a URL (http, https), a local file path (e.g., C:/, /users/), or raw text meant to be used as context. + - A 'query' is a question or a command asking to perform an action (e.g., 'summarize', 'what is?', 'make notes', 'create a mind map', 'create a podcast'). + - 'exit' means the user wants to end the conversation. + Respond with ONLY 'resource', 'query', or 'exit'.`; + + const response = await llm.invoke([new SystemMessage(systemPrompt), new HumanMessage(lastInput)]); + + return { routeDecision: response.content }; +}; + +// Handles resource ingestion. It uses an LLM with ingestion tools to process URLs, PDFs and text. +const ingestion_node = async (state) => { + const { lastInput } = state; + const systemPrompt = "You are an expert at identifying all resources (URLs, file paths, raw text) in a user's input. For each identified resource, call the appropriate ingestion tool."; + + const response = await llmWithIngestionTools.invoke([new SystemMessage(systemPrompt), new HumanMessage(lastInput)]); + + // The response will contain tool calls that the next node will execute. + return { messages: [response] }; +}; + +// Processes tool calls from ingestion_node ,splits & stores text into Chroma vector DB +const ingestion_tool_node = async (state) => { + const { messages, userId } = state; + const lastMessage = messages[messages.length - 1]; + + // This is the list of tool calls the LLM wants to make + const toolCalls = lastMessage.tool_calls; + + // Create a map of our tools for easy access + const toolMap = new Map(ingestionTools.map(tool => [tool.name, tool])); + const toolOutputs = []; + const toolMessages = []; + + // Loop through each tool call and invoke it directly + for (const call of toolCalls) { + const toolToCall = toolMap.get(call.name); + if (toolToCall) { + const output = await toolToCall.invoke(call.args); // Invoke the tool with the provided arguments + toolOutputs.push(output); + toolMessages.push(new ToolMessage({ tool_call_id: call.id, content: `Successfully processed ${call.args}` })); + } + } + // Aggregate all the text extracted by the tools + const newContent = toolOutputs.join("\n\n"); + + // --- Vectorization Step --- + + // Split text into overlapping chunks for better retrieval accuracy + const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 500, chunkOverlap: 50 }); + /* RecursiveCharacterTextSplitter : + The splitter uses a list of separators (e.g., ["\n\n", "\n", " ", ""]) and attempts to split the text using the first separator in the list. + If a resulting chunk is smaller than the specified chunkSize, it's considered a valid chunk. + If a chunk is larger than chunkSize, the splitter recursively applies the next separator in the list to that chunk, trying to break it down further until all chunks are within the size limit. + */ + const docs = await splitter.splitDocuments([ + new Document({ pageContent: newContent, metadata: { userId, source: "user_upload" } }) + ]); + + const collectionName = `agent_docs_${userId}`; // Unique collection per user + const vectorStore = new Chroma(embeddings, { collectionName }); + await vectorStore.addDocuments(docs); // Add the new documents to the vector store + console.log(`✅ Content chunked and added to the vector store for user: ${userId}`); + + const confirmationMessage = new AIMessage("✅ Content has been processed and stored. What would you like to do with it?"); + + return { + messages: [...toolMessages, confirmationMessage], + vectorStore: vectorStore, // Pass the vector store instance back to the state + }; +}; + +// Handles user queries. It can answer from context, from general knowledge, or use processing tools. +// It will first try to retrieve relevant documents from the vector store based on the user's query. +const response_query_node = async (state) => { + const { lastInput, messages, vectorStore } = state; + + let retrievedContext = "No relevant context found."; + const lastMessage = messages.length > 0 ? messages[messages.length - 1] : null; // Get the last message if it exists + + // If we have a vector store, perform a similarity search to get relevant context + if (vectorStore) { + if (!lastMessage || lastMessage._getType() !== "tool") { + console.log("✅ Retrieving relevant context from vector store..."); + } + // Convert the user's natural-language query into an embedding vector + const queryVector = await embeddings.embedQuery(lastInput); + // Perform a similarity search in the vector store using the query embedding and returning the top 2 most relevant chunks + const results = await vectorStore.similaritySearchVectorWithScore([queryVector], 2); + // Extract the text content from the retrieved documents. + retrievedContext = results.map(result => result[0].pageContent).join("\n\n"); + } + + const systemPrompt = `You are a helpful and expert AI assistant. You MUST answer the user's query based ONLY on the provided context below. + Context: + --- + ${retrievedContext} + --- + If the context is not sufficient, you may use a tool. Your reasoning process: + 1. Analyze the user's query. + 2. If the context provides enough information, formulate a final answer. + 3. If not, you may call a tool (Summarization, NoteTaking, etc.) using the retrieved context to process it further. + 4. After a tool runs, evaluate its output to form a final answer. + IMPORTANT: Once you have a satisfactory answer, respond directly to the user without tool calls.`; + + const conversation = [new SystemMessage(systemPrompt), ...messages, new HumanMessage(lastInput)]; + const response = await llmWithProcessingTools.invoke(conversation); // The response may contain tool calls or a final answer. + + return { messages: [new HumanMessage(lastInput), response] }; +}; + +// 4. GRAPH ROUTING AND CONSTRUCTION + +const route_planner = (state) => { + const { routeDecision } = state; + if (routeDecision.includes("resource")) return "ingestion_node"; + if (routeDecision.includes("query")) return "response_query_node"; + return END; +}; + +const route_after_query = (state) => { + const { messages } = state; + const lastMessage = messages[messages.length - 1]; + // If the last message contains tool calls, route to the processing tool node. + if (lastMessage.tool_calls?.length) { + return "processing_tool_node"; + } + // If the last message is a final answer (no tool calls), end the conversation. + return END ; +}; + +const workflow = new StateGraph({ channels: agentState }); + +workflow.addNode("planner_node", planner_node); +workflow.addNode("ingestion_node", ingestion_node); +workflow.addNode("ingestion_tool_node", ingestion_tool_node); +workflow.addNode("response_query_node", response_query_node); +workflow.addNode("processing_tool_node", processingToolNode); + +workflow.addEdge("__start__", "planner_node"); +workflow.addConditionalEdges( + "planner_node", + route_planner, + { + "ingestion_node": "ingestion_node", + "response_query_node": "response_query_node", + "__end__": END + } +); + +// Ingestion Spoke +workflow.addEdge("ingestion_node", "ingestion_tool_node"); +workflow.addEdge("ingestion_tool_node", END); + +// Query Spoke +workflow.addConditionalEdges( + "response_query_node", + route_after_query, + { + "processing_tool_node": "processing_tool_node", + "__end__" : END // End if final answer + } +); +workflow.addEdge("processing_tool_node", "response_query_node"); // Loop back to process tool results +const app = workflow.compile(); + +// 5. GRAPH VISUALIZATION EXPORT FUNCTION + +async function exportGraphImage(compiledGraph, fileName = "graph.png") { + // Generate a visual representation of the graph structure and save it as a PNG image. + + try { + const drawableGraph = await compiledGraph.getGraphAsync(); // Get drawable graph representation + const image = await drawableGraph.drawMermaidPng();// Generate PNG image using Mermaid + + if (Buffer.isBuffer(image)) { + fs.writeFileSync(fileName, image); + } else if (typeof image.arrayBuffer === "function") { + const buffer = Buffer.from(await image.arrayBuffer()); + fs.writeFileSync(fileName, buffer); + } + + console.log(`✅ Graph structure exported successfully as ${fileName}`); + } catch (error) { + console.error("❌ Error generating graph visualization:", error); + } +} + +await exportGraphImage(app, "./assets/agent_graph.png"); + +// 6. AGENT EXECUTION LOOP + +const agentCall = async () => { + console.log("🤖 Hello! I am your AI assistant. You can provide a resource (URL, PDF, text) or ask a query."); + + // This Map will act as our in-memory storage for all conversation states. + const conversationStates = new Map(); + + while (true) { + // Ask for a User ID to simulate different users or conversation threads. + const userId = prompt("Enter a User ID to start or continue a conversation (e.g., 'user_1'): "); + if (userId.toLowerCase() === "exit") { + console.log("🤖 Goodbye!"); + break; + } + + console.log(`--- Starting/Resuming Conversation with ${userId} ---`); + + // Inner loop for the active conversation thread. + while (true) { + const userInput = prompt(`${userId}: `); + if (userInput.toLowerCase() === "exit") { + console.log(`--- Pausing conversation with ${userId} ---`); + break; // Exit inner loop to switch users + } + + // 1. Get the state for the current thread, or create a new one if it's the first time. + const currentState = conversationStates.get(userId) || { + messages: [], + userId: userId, // Pass the userId into the state + }; + + // 2. Invoke the graph with the current thread's state. + const result = await app.invoke({ ...currentState, lastInput: userInput }); + + // 3. 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"@chroma-core/default-embed": "^0.1.8", + "@google-cloud/text-to-speech": "^6.2.0", + "@huggingface/transformers": "^3.7.3", + "@langchain/community": "^0.3.50", + "@langchain/core": "^0.3.68", + "@langchain/groq": "^0.2.3", + "@langchain/langgraph": "^0.4.3", + "chromadb": "^3.0.15", + "html-to-text": "^9.0.5", + "jsdom": "^26.1.0", + "pdf-parse": "^1.1.1", + "prompt-sync": "^4.2.0", + "youtubei.js": "^15.0.1", + "zod": "^3.25.76" + } +} diff --git a/langGraph.js/Agent-5/tools/ingestion_tools.js b/langGraph.js/Agent-5/tools/ingestion_tools.js new file mode 100644 index 0000000..51bce17 --- /dev/null +++ b/langGraph.js/Agent-5/tools/ingestion_tools.js @@ -0,0 +1,77 @@ +import { tool } from "@langchain/core/tools"; +import { z } from "zod"; +import { YoutubeLoader } from "@langchain/community/document_loaders/web/youtube"; +import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf"; +import { RecursiveUrlLoader } from "@langchain/community/document_loaders/web/recursive_url"; +import { compile } from "html-to-text"; + +export function createIngestionTools() { + + const youtubeTool = tool(async (url) => { + // Ingest YouTube video transcript + try { + console.log(`[Tool] Ingesting YouTube URL: ${url}`); + // YoutubeLoader- Fetches and parses transcripts from a YouTube video. + const loader = YoutubeLoader.createFromUrl(url, { language: "en", addVideoInfo: false }); + // `addVideoInfo: false` avoids loading metadata, reducing output noise. + + const docs = await loader.load(); + return docs.map(doc => doc.pageContent).join('\n\n'); // Combine pages into a single text block. + } catch (e) { return `Error loading YouTube URL: ${e.message}`; } + }, { + name: "youtube_ingestion_tool", + description: "Extracts the transcript from a YouTube video URL.", + schema: z.string().describe("The full URL of the YouTube video."), + }); + + const webpageTool = tool(async (url) => { + // Ingest webpage content + try { + console.log(`[Tool] Ingesting Webpage URL: ${url}`); + + /* RecursiveUrlLoader: + Loads webpage content by crawling a URL and optionally following links on the webpage recursively. + It fetches the HTML content of each page, processes it into text using a custom extractor, + and returns it as a collection of documents. The maxDepth option controls how deep the crawler navigates. + `compile` converts HTML to clean plaintext. + The wordwrap: 130 option ensures that long lines are wrapped at approximately 130 characters, producing clean, formatted text. + `maxDepth: 0` prevents recursive crawling (extracts only the provided page). + */ + const loader = new RecursiveUrlLoader(url, { extractor: compile({ wordwrap: 130 }), maxDepth: 0 }); + const docs = await loader.load(); + return docs.map(doc => doc.pageContent).join('\n\n'); + } catch (e) { return `Error loading webpage: ${e.message}`; } + }, { + name: "webpage_ingestion_tool", + description: "Extracts the text content from a webpage URL.", + schema: z.string().describe("The full URL of the webpage."), + }); + + const pdfTool = tool(async (filePath) => { + // Loads the PDF from a file path and extracts its text + try { + console.log(`[Tool] Ingesting PDF: ${filePath}`); + // PDFLoader- Loads and parses local PDF files, extracting their text content page by page. + const loader = new PDFLoader(filePath); + const docs = await loader.load(); + return docs.map(doc => doc.pageContent).join('\n\n'); + } catch (e) { return `Error loading PDF: ${e.message}`; } + }, { + name: "pdf_ingestion_tool", + description: "Extracts the text content from a local PDF file path.", + schema: z.string().describe("The local file path to the PDF."), + }); + + const textContentTool = tool(async (text) => { + // Ingest raw text directly from user input. Useful for pasting content directly instead of using files or URLs. + console.log("[Tool] Storing direct text input."); + return text; + }, { + name: "text_ingestion_tool", + description: "Stores direct text provided by the user as a resource.", + schema: z.string().describe("The raw text content to be used as a resource."), + }); + + // Export all ingestion tools so they can be registered with an agent. + return [youtubeTool, webpageTool, pdfTool, textContentTool]; +} \ No newline at end of file diff --git a/langGraph.js/Agent-5/tools/processing_tools.js b/langGraph.js/Agent-5/tools/processing_tools.js new file mode 100644 index 0000000..baa031d --- /dev/null +++ b/langGraph.js/Agent-5/tools/processing_tools.js @@ -0,0 +1,168 @@ +import { HumanMessage, SystemMessage } from "@langchain/core/messages"; +import { tool } from "@langchain/core/tools"; +import fs from "fs"; +import util from "util"; +import textToSpeech from "@google-cloud/text-to-speech"; +import { z } from "zod"; + +export function createTools(llm) { + // docContent: Content extracted from the source material + //query : The user's query regarding the source material + + const mindMapTool = tool( // creates a mind map of the source material + async ({ docContent, query }) => { + + const systemMessage = new SystemMessage(`You are an AI Assistant that helps users create a mind map based on the content provided. + Return ONLY valid JSON in this structure: + { + "title": "", + "topics": [ + { + "title": "", + "subtopics": [ + { "title": "", "subsubtopics": [{"title...//if applicable"}] } + ] + } + ] + }` + ); + + const response = await llm.invoke([systemMessage ,new HumanMessage(`content : ${docContent} where the query was ${query}`)]); + const mindMap = response.content; + + // Save generated mind map JSON to a file for user access. + await fs.promises.writeFile("mind_map.txt", mindMap); + return "✅ Mind map saved to mind_map.txt\n" + mindMap; + }, + { + name: "MindMapCreation", + description: "Generate a hierarchical mind-map JSON based on the provided content and user query.", + schema: z.object({ + query: z.string().describe("The user's query regarding the source material"), + docContent: z.string().describe("Content extracted from the source material"), + }), + } + ); + + const podcastTool = tool( + // Podcast Creation Tool- Generates a two-speaker podcast script (Host + Guest) using the LLM, then converts it into audio using Google Cloud Text-to-Speech. + async ({ docContent, query }) => { + // --- STEP 1: Update the LLM Prompt --- + // Instruct the LLM to write a script with clear speaker labels. + const systemMessage = new SystemMessage( + `You are a professional podcast scriptwriter. Your task is to create an engaging conversational script between two speakers (a Host and a Guest) based on the provided content. + + IMPORTANT: You MUST format the script with speaker labels on each line. For example: + Host: "Welcome to the show." + Guest: "It's great to be here." + + Your entire response must be ONLY the script itself. Do not include any other text.` + ); + + const llmResponse = await llm.invoke([ + systemMessage, + new HumanMessage(`Create a podcast script from this content: ${docContent}, focusing on the query: "${query}"`) + ]); + const podcastScript = llmResponse.content; + + // --- STEP 2: Convert the Script to SSML --- + // We'll parse the script and wrap each line in SSML tags to assign voices. + const lines = podcastScript.split('\n').filter(line => line.trim() !== ''); + let ssmlBody = ''; + + for (const line of lines) { + if (line.startsWith('Host:')) { + // Assign a male voice to the Host + const dialogue = line.replace('Host:', '').trim(); + ssmlBody += `${dialogue}`; + } else if (line.startsWith('Guest:')) { + // Assign a female voice to the Guest + const dialogue = line.replace('Guest:', '').trim(); + ssmlBody += `${dialogue}`; + } + } + + // The final SSML string must be wrapped in tags. + const finalSSML = `${ssmlBody}`; + + // --- STEP 3: Update the Google Cloud TTS API Call --- + const client = new textToSpeech.TextToSpeechClient({ keyFilename: "gcp-tts.json" }); // credentials file used here + const request = { + // Tell the API we are sending SSML, not plain text + input: { ssml: finalSSML }, + // The voice config here acts as a default but is overridden by the tags in our SSML + voice: { languageCode: "en-US" }, + audioConfig: { audioEncoding: "MP3" }, + }; + + // Send synthesis request to Google Cloud + const [result] = await client.synthesizeSpeech(request); + + // Write resulting MP3 to disk + const writeFile = util.promisify(fs.writeFile); + await writeFile("podcast_episode_two_voice.mp3", result.audioContent, "binary"); + + return "✅ A two-voice podcast has been created and saved to podcast_episode_two_voice.mp3"; + }, + { + name: "PodcastCreation", + description: "Generates a two-voice conversational podcast script based on the provided content and user query, then converts it to audio.", + schema: z.object({ + query: z.string().describe("The user's query regarding the source material"), + docContent: z.string().describe("Content extracted from the source material"), + }), + } + ); + + const noteTakingTool= tool( // creates bullet notes of the source material + async ({docContent, query}) => { + + // LLM prompt instructing it to produce brief and structured bullet notes. + const response = await llm.invoke([ + new SystemMessage("You are an AI assistant that helps user make bullet points notes of the context provided. Based on the content provided, give proper brief and concise notes"), + new HumanMessage(`Here is the content: ${docContent} and here is the query used: ${query}`) + ]); + + return `Here are the notes you requested:\n${response.content}`; + },{ + name: "NoteTaking", + description: "Generates concise notes based on the provided content and user query.", + schema: z.object({ + docContent: z.string().describe("Content extracted from the source material"), + query: z.string().describe("The user's query regarding the source material"), + }), + } + ); + + const summarizationTool = tool( // summarizes the source material + async ({docContent, query}) => { + + // Standard summarization prompt for the LLM. + const summaryPrompt = [ + { + role: "system", + content: + "Summarize the following text clearly and concisely for the user.", + }, + { + role: "user", + content: `Content: ${docContent} and here is the query used: ${query}`, + }, + ]; + + const response = await llm.invoke(summaryPrompt); + return `Here is the summary you requested:\n${response.content}`; + }, + { + name: "Summarization", + description: "Summarizes the provided content based on the user's query.", + schema: z.object({ + query: z.string().describe("The user's query regarding the source material"), + docContent : z.string().describe("Content extracted from the source material"), + }), + } + ); + + // Return all tools so they can be registered with the agent. + return [mindMapTool, podcastTool, noteTakingTool, summarizationTool]; +} \ No newline at end of file diff --git a/langGraph.js/README.md b/langGraph.js/README.md new file mode 100644 index 0000000..80c2805 --- /dev/null +++ b/langGraph.js/README.md @@ -0,0 +1,35 @@ +# LangGraph.js + +Welcome to the LangGraph.js repository! This is a collection of 5 distinct AI agents, each built using [Node.js](https://nodejs.org/), [LangChain.js](https://js.langchain.com/docs/introduction/), and the [LangGraph.js](https://langchain-ai.github.io/langgraphjs/) library. + +> Want to understand the problem each agent solves? +> [View the Official Problem Statement](https://github.com/cladius/agentic-ai/blob/master/sample_problem.md) + +## What is LangGraph.js? +[LangGraph.js](https://langchain-ai.github.io/langgraphjs/) is a JavaScript library for building **robust** and **stateful** applications with Large Language Models (LLMs). It lets you design workflows as **graphs**, where each **node** performs a task—like an agent action or tool call—updating a shared **state** (containing inputs, intermediate results, or conversation history), and **edges** control the flow between nodes, including conditional paths. + +## Key Features +- **Graph-based Workflow**: Model complex agent behaviors using directed graphs(nodes+edges). + +- **Stateful Execution**: Maintains a shared, updatable state across all nodes (steps), enabling agents to retain context and memory. + +- **Multi-Agent Coordination**: Facilitates the creation of workflows where different agents can call upon each other or specialized tools. + +- **Flexible Logic**: Supports loops and conditional paths, allowing agents to repeat steps or adapt based on results. + +- **Extensible**: Easily add new nodes, tools, or agents to expand functionality. + +- **Human-in-the-Loop**: Supports integration of human oversight and intervention, allowing for inspection and modification of agent state during execution. + +## Prerequisites +- [Node.js (v18+)](https://nodejs.org/) - runtime environment +- [GROQ_API_KEY](https://console.groq.com/keys) - for Groq LLMs +- (Optional) [TAVILY_API_KEY](https://app.tavily.com/home) - real-time search (Agent-3) +- (Optional) [Docker Desktop](https://www.docker.com/products/docker-desktop) - required for ChromaDB vector store (Agents 4 & 5) +- (Optional) Google Cloud service account JSON - for podcast [Text-to-Speech API](https://cloud.google.com/text-to-speech?hl=en) (Agent-5) + +--- + +### **Documentation** +Check out the [LangGraph_documentation](https://github.com/user-attachments/files/23729518/LangGraph_Report.pdf) +