From be34fb2c372b9aa39f3281a37ecf9cd4a455c0a5 Mon Sep 17 00:00:00 2001 From: Delacrobix Date: Mon, 10 Nov 2025 11:46:15 -0500 Subject: [PATCH 1/2] supporging blog content human-in-the-loop-with-langgraph-and-elasticsearch --- .../README.md | 56 + .../dataIngestion.ts | 141 + .../dataset.json | 152 + .../main.ts | 242 ++ .../package-lock.json | 3383 +++++++++++++++++ .../package.json | 22 + .../workflow_graph.png | Bin 0 -> 26754 bytes 7 files changed, 3996 insertions(+) create mode 100644 supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/README.md create mode 100644 supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataIngestion.ts create mode 100644 supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataset.json create mode 100644 supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/main.ts create mode 100644 supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/package-lock.json create mode 100644 supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/package.json create mode 100644 supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/workflow_graph.png diff --git a/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/README.md b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/README.md new file mode 100644 index 00000000..b021ccfd --- /dev/null +++ b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/README.md @@ -0,0 +1,56 @@ +# LangGraph + Elasticsearch Human-in-the-Loop + +Flight search application using LangGraph for human-in-the-loop workflow and Elasticsearch for vector search. + +## Prerequisites + +- Node.js 18+ +- Elasticsearch instance +- OpenAI API key + +## Installation + +### Quick Install + +```bash +npm install +``` + +### Manual Install (Alternative) + +```bash +npm install @elastic/elasticsearch @langchain/community @langchain/core @langchain/langgraph @langchain/openai dotenv --legacy-peer-deps +npm install --save-dev tsx +``` + +## Configuration + +Create a `.env` file in the root directory: + +```env +ELASTICSEARCH_ENDPOINT=https://your-elasticsearch-instance.com +ELASTICSEARCH_API_KEY=your-api-key +OPENAI_API_KEY=your-openai-api-key +``` + +## Usage + +```bash +npm start +``` + +## Features + +- 🔍 Vector search with Elasticsearch +- 🤖 LLM-powered natural language selection +- 👤 Human-in-the-loop workflow with LangGraph +- đź“Š Workflow visualization (generates `workflow_graph.png`) + +## Workflow + +1. **Retrieve Flights** - Search Elasticsearch with vector similarity +2. **Evaluate Results** - Auto-select if 1 result, show options if multiple +3. **Show Results** - Display flight options to user +4. **Request User Choice** - Pause workflow for user input (HITL) +5. **Disambiguate & Answer** - Use LLM to interpret selection and return final answer + diff --git a/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataIngestion.ts b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataIngestion.ts new file mode 100644 index 00000000..7d4a7939 --- /dev/null +++ b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataIngestion.ts @@ -0,0 +1,141 @@ +import { ElasticVectorSearch } from "@langchain/community/vectorstores/elasticsearch"; +import { OpenAIEmbeddings } from "@langchain/openai"; +import { Client } from "@elastic/elasticsearch"; +import { readFile } from "node:fs/promises"; +import dotenv from "dotenv"; + +dotenv.config(); + +const VECTOR_INDEX = "flights-offerings"; + +// Types +export interface DocumentMetadata { + from_city: string; + to_city: string; + airport_code: string; + airport_name: string; + country: string; + airline: string; + date: string; + price: number; + time_approx: string; + title: string; +} + +export interface Document { + pageContent: string; + metadata: DocumentMetadata; +} + +interface RawDocument { + pageContent?: string; + text?: string; + metadata?: DocumentMetadata; +} + +const esClient = new Client({ + node: process.env.ELASTICSEARCH_ENDPOINT!, + auth: { + apiKey: process.env.ELASTICSEARCH_API_KEY!, + }, +}); + +const embeddings = new OpenAIEmbeddings({ + model: "text-embedding-3-small", +}); + +const vectorStore = new ElasticVectorSearch(embeddings, { + client: esClient, + indexName: VECTOR_INDEX, +}); + +/** + * Load dataset from a JSON file + * @param path - Path to the JSON file + * @returns Array of documents with pageContent and metadata + */ +export async function loadDataset(path: string): Promise { + const raw = await readFile(path, "utf-8"); + const data: RawDocument[] = JSON.parse(raw); + + return data.map((d) => ({ + pageContent: String(d.pageContent ?? d.text ?? ""), + metadata: (d.metadata ?? {}) as DocumentMetadata, + })); +} + +/** + * Ingest data into Elasticsearch vector store + * Creates the index if it doesn't exist and loads initial dataset + */ +export async function ingestData(): Promise { + const vectorExists = await esClient.indices.exists({ index: VECTOR_INDEX }); + + if (!vectorExists) { + console.log("CREATING VECTOR INDEX..."); + + await esClient.indices.create({ + index: VECTOR_INDEX, + mappings: { + properties: { + text: { type: "text" }, + embedding: { + type: "dense_vector", + dims: 1536, + index: true, + similarity: "cosine", + }, + metadata: { + type: "object", + properties: { + from_city: { type: "keyword" }, + to_city: { type: "keyword" }, + airport_code: { type: "keyword" }, + airport_name: { + type: "text", + fields: { + keyword: { type: "keyword" }, + }, + }, + country: { type: "keyword" }, + airline: { type: "keyword" }, + date: { type: "date" }, + price: { type: "integer" }, + time_approx: { type: "keyword" }, + title: { + type: "text", + fields: { + keyword: { type: "keyword" }, + }, + }, + }, + }, + }, + }, + }); + } + + const indexExists = await esClient.indices.exists({ index: VECTOR_INDEX }); + + if (indexExists) { + const indexCount = await esClient.count({ index: VECTOR_INDEX }); + const documentCount = indexCount.count; + + // Only ingest if index is empty + if (documentCount > 0) { + console.log( + `Index already contains ${documentCount} documents. Skipping ingestion.` + ); + return; + } + + console.log("INGESTING DATASET..."); + const datasetPath = "./dataset.json"; + const initialDocs = await loadDataset(datasetPath).catch(() => []); + + await vectorStore.addDocuments(initialDocs); + console.log(`âś… Successfully ingested ${initialDocs.length} documents`); + } +} + +export { VECTOR_INDEX }; diff --git a/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataset.json b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataset.json new file mode 100644 index 00000000..46a4b408 --- /dev/null +++ b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataset.json @@ -0,0 +1,152 @@ +[ + { + "pageContent": "Ticket: MedellĂ­n (MDE) → Sao Paulo (GRU). 1 stop. Airline: AndeanSky.", + "metadata": { + "from_city": "MedellĂ­n", + "to_city": "Sao Paulo", + "airport_code": "GRU", + "airport_name": "Guarulhos International", + "country": "Brazil", + "airline": "AndeanSky", + "date": "2025-10-10", + "price": 480, + "time_approx": "7h 30m", + "title": "MDE → GRU (AndeanSky)" + } + }, + { + "pageContent": "Ticket: MedellĂ­n (MDE) → Sao Paulo (CGH). 1 stop. Airline: CoffeeAir.", + "metadata": { + "from_city": "MedellĂ­n", + "to_city": "Sao Paulo", + "airport_code": "CGH", + "airport_name": "Congonhas", + "country": "Brazil", + "airline": "CoffeeAir", + "date": "2025-10-10", + "price": 455, + "time_approx": "8h 05m", + "title": "MDE → CGH (CoffeeAir)" + } + }, + { + "pageContent": "Ticket: MedellĂ­n (MDE) → Tokyo (HND). 2 stops. Airline: CondorJet.", + "metadata": { + "from_city": "MedellĂ­n", + "to_city": "Tokyo", + "airport_code": "HND", + "airport_name": "Haneda", + "country": "Japan", + "airline": "CondorJet", + "date": "2025-11-05", + "price": 1290, + "time_approx": "22h 10m", + "title": "MDE → HND (CondorJet)" + } + }, + { + "pageContent": "Ticket: MedellĂ­n (MDE) → Tokyo (NRT). 1–2 stops. Airline: CaribeWings.", + "metadata": { + "from_city": "MedellĂ­n", + "to_city": "Tokyo", + "airport_code": "NRT", + "airport_name": "Narita", + "country": "Japan", + "airline": "CaribeWings", + "date": "2025-11-05", + "price": 1215, + "time_approx": "23h 30m", + "title": "MDE → NRT (CaribeWings)" + } + }, + { + "pageContent": "Ticket: MedellĂ­n (MDE) → New York (JFK). 1 stop. Airline: AndeanSky.", + "metadata": { + "from_city": "MedellĂ­n", + "to_city": "New York", + "airport_code": "JFK", + "airport_name": "John F. Kennedy International", + "country": "USA", + "airline": "AndeanSky", + "date": "2025-12-01", + "price": 340, + "time_approx": "6h 40m", + "title": "MDE → JFK (AndeanSky)" + } + }, + { + "pageContent": "Ticket: MedellĂ­n (MDE) → New York (LGA). 1 stop. Airline: CoffeeAir.", + "metadata": { + "from_city": "MedellĂ­n", + "to_city": "New York", + "airport_code": "LGA", + "airport_name": "LaGuardia", + "country": "USA", + "airline": "CoffeeAir", + "date": "2025-12-01", + "price": 325, + "time_approx": "6h 55m", + "title": "MDE → LGA (CoffeeAir)" + } + }, + { + "pageContent": "Ticket: MedellĂ­n (MDE) → London (LHR). 1 stop. Airline: CondorJet.", + "metadata": { + "from_city": "MedellĂ­n", + "to_city": "London", + "airport_code": "LHR", + "airport_name": "Heathrow", + "country": "UK", + "airline": "CondorJet", + "date": "2026-01-15", + "price": 890, + "time_approx": "14h 30m", + "title": "MDE → LHR (CondorJet)" + } + }, + { + "pageContent": "Ticket: MedellĂ­n (MDE) → London (LGW). 1–2 stops. Airline: CaribeWings.", + "metadata": { + "from_city": "MedellĂ­n", + "to_city": "London", + "airport_code": "LGW", + "airport_name": "Gatwick", + "country": "UK", + "airline": "CaribeWings", + "date": "2026-01-15", + "price": 760, + "time_approx": "15h 10m", + "title": "MDE → LGW (CaribeWings)" + } + }, + { + "pageContent": "Ticket: MedellĂ­n (MDE) → Paris (CDG). 1 stop. Airline: CoffeeAir.", + "metadata": { + "from_city": "MedellĂ­n", + "to_city": "Paris", + "airport_code": "CDG", + "airport_name": "Charles de Gaulle", + "country": "France", + "airline": "CoffeeAir", + "date": "2025-10-22", + "price": 720, + "time_approx": "13h 50m", + "title": "MDE → CDG (CoffeeAir)" + } + }, + { + "pageContent": "Ticket: MedellĂ­n (MDE) → Paris (ORY). 1 stop. Airline: AndeanSky.", + "metadata": { + "from_city": "MedellĂ­n", + "to_city": "Paris", + "airport_code": "ORY", + "airport_name": "Orly", + "country": "France", + "airline": "AndeanSky", + "date": "2025-10-22", + "price": 695, + "time_approx": "13h 20m", + "title": "MDE → ORY (AndeanSky)" + } + } +] diff --git a/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/main.ts b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/main.ts new file mode 100644 index 00000000..41f3593d --- /dev/null +++ b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/main.ts @@ -0,0 +1,242 @@ +import { + StateGraph, + Annotation, + interrupt, + Command, + MemorySaver, +} from "@langchain/langgraph"; +import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai"; +import { ElasticVectorSearch } from "@langchain/community/vectorstores/elasticsearch"; +import { Client } from "@elastic/elasticsearch"; +import { writeFileSync } from "node:fs"; +import readline from "node:readline"; +import { ingestData, Document, DocumentMetadata } from "./dataIngestion.ts"; + +const VECTOR_INDEX = "flights-offerings"; + +const llm = new ChatOpenAI({ model: "gpt-4o-mini" }); +const embeddings = new OpenAIEmbeddings({ + model: "text-embedding-3-small", +}); + +const esClient = new Client({ + node: process.env.ELASTICSEARCH_ENDPOINT!, + auth: { + apiKey: process.env.ELASTICSEARCH_API_KEY!, + }, +}); + +const vectorStore = new ElasticVectorSearch(embeddings, { + client: esClient, + indexName: VECTOR_INDEX, +}); + +// Define the state schema for application workflow +const SupportState = Annotation.Root({ + input: Annotation(), + candidates: Annotation(), + userChoice: Annotation(), + selected: Annotation(), + final: Annotation(), +}); + +// Node 1: Retrieve data from Elasticsearch +async function retrieveFlights(state: typeof SupportState.State) { + const results = await vectorStore.similaritySearch(state.input, 2); + const candidates = results.map((d) => d as Document); + + console.log(`đź“‹ Found ${candidates.length} different flights`); + return { candidates }; +} + +// Node 2: Evaluate if there are 1 or multiple responses +function evaluateResults(state: typeof SupportState.State) { + const candidates = state.candidates || []; + + // If there is 1 result, auto-select it + if (candidates.length === 1) { + const metadata = candidates[0].metadata || {}; + + return { + selected: candidates[0], + final: formatFlightDetails(metadata), + }; + } + + return { candidates }; +} + +// Node 3: Request user choice (separate from showing) +function requestUserChoice() { + const userChoice = interrupt({ + question: `Which flight do you prefer?:`, + }); + + return { userChoice }; +} + +// Node 4: Disambiguate user choice and provide final answer +async function disambiguateAndAnswer(state: typeof SupportState.State) { + const candidates = state.candidates || []; + const userInput = state.userChoice || ""; + + const prompt = ` + Based on the user's response: "${userInput}" + + These are the available flights: + ${candidates + .map( + (d, i) => + `${i}. ${d.metadata?.title} - ${d.metadata?.to_city} (${d.metadata?.airport_code}) - ${d.metadata?.airline} - $${d.metadata?.price} - ${d.metadata?.time_approx}` + ) + .join("\n")} + + Which flight is the user selecting? Respond ONLY with the flight number (1, 2, or 3). + `; + + const llmResponse = await llm.invoke([ + { + role: "system", + content: + "You are an assistant that interprets user selection. Respond ONLY with the selected flight number.", + }, + { role: "user", content: prompt }, + ]); + + const selectedNumber = Number.parseInt(llmResponse.content as string, 10); // Convert to zero-based index + const selectedFlight = candidates[selectedNumber] ?? candidates[0]; // Fallback to first option + + return { + selected: selectedFlight, + final: formatFlightDetails(selectedFlight.metadata), + }; +} + +// Node 5: Show results only +function showResults(state: typeof SupportState.State) { + const candidates = state.candidates || []; + const formattedOptions = candidates + .map((d: Document, index: number) => { + const m = d.metadata || {}; + + return `${index + 1}. ${m.title} - ${m.to_city} - ${m.airport_name}(${ + m.airport_code + }) airport - ${m.airline} - $${m.price} - ${m.time_approx}`; + }) + .join("\n"); + + console.log(`\nđź“‹ Flights found:\n${formattedOptions}\n`); + + return state; +} + +// Helper function to format flight details +function formatFlightDetails(metadata: DocumentMetadata): string { + return `Selected flight: ${metadata.title} - ${metadata.airline} + From: ${metadata.from_city} (${ + metadata.from_city?.slice(0, 3).toUpperCase() || "N/A" + }) + To: ${metadata.to_city} (${metadata.airport_code}) + Airport: ${metadata.airport_name} + Price: $${metadata.price} + Duration: ${metadata.time_approx} + Date: ${metadata.date}`; +} + +// Build the graph +const workflow = new StateGraph(SupportState) + .addNode("retrieveFlights", retrieveFlights) + .addNode("evaluateResults", evaluateResults) + .addNode("showResults", showResults) + .addNode("requestUserChoice", requestUserChoice) + .addNode("disambiguateAndAnswer", disambiguateAndAnswer) + .addEdge("__start__", "retrieveFlights") + .addEdge("retrieveFlights", "evaluateResults") + .addConditionalEdges( + "evaluateResults", + (state: typeof SupportState.State) => { + if (state.final) return "complete"; // 0 or 1 result + return "multiple"; // multiple results + }, + { + complete: "__end__", + multiple: "showResults", + } + ) + .addEdge("showResults", "requestUserChoice") + .addEdge("requestUserChoice", "disambiguateAndAnswer") + .addEdge("disambiguateAndAnswer", "__end__"); + +/** + * Get user input from the command line + * @param question - Question to ask the user + * @returns User's answer + */ +function getUserInput(question: string): Promise { + const rl = readline.createInterface({ + input: process.stdin, + output: process.stdout, + }); + + return new Promise((resolve) => { + rl.question(question, (answer) => { + rl.close(); + resolve(answer.trim()); + }); + }); +} + +/** + * Save workflow graph as PNG image + * @param app - Compiled workflow application + */ +async function saveGraphImage(app: any): Promise { + try { + const graph = app.getGraph(); + const graphImage = await graph.drawMermaidPng(); + const graphArrayBuffer = await graphImage.arrayBuffer(); + + const filePath = "./workflow_graph.png"; + writeFileSync(filePath, new Uint8Array(graphArrayBuffer)); + console.log(`đź“Š Workflow graph saved as: ${filePath}`); + } catch (error) { + console.log("⚠️ Could not save graph image:", (error as Error).message); + } +} + +/** + * Main execution function + */ +async function main() { + // Ingest data + await ingestData(); + + // Compile workflow + const app = workflow.compile({ checkpointer: new MemorySaver() }); + const config = { configurable: { thread_id: "hitl-thread" } }; + + // Save graph image + await saveGraphImage(app); + + // Execute workflow + const question = "Flights to Tokyo"; // User query + console.log(`🔍 SEARCHING USER QUESTION: "${question}"\n`); + + let currentState = await app.invoke({ input: question }, config); + + // Handle interruption + if ((currentState as any).__interrupt__?.length > 0) { + console.log("\nđź’­ APPLICATION PAUSED WAITING FOR USER INPUT..."); + const userChoice = await getUserInput("👤 CHOICE ONE OPTION: "); + + currentState = await app.invoke( + new Command({ resume: userChoice }), + config + ); + } + + console.log("\nâś… Final result: \n", currentState.final); +} + +// Run main function +await main(); diff --git a/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/package-lock.json b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/package-lock.json new file mode 100644 index 00000000..75ed5141 --- /dev/null +++ b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/package-lock.json @@ -0,0 +1,3383 @@ +{ + "name": "LangGraph Elasticsearch", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "LangGraph Elasticsearch", + "version": "1.0.0", + "license": "MIT", + "dependencies": { + "@elastic/elasticsearch": "^9.2.0", + "@langchain/community": "^0.3.57", + "@langchain/core": "^0.3.79", + "@langchain/langgraph": "^0.4.9", + "@langchain/openai": "^0.6.16", + "dotenv": "^16.6.1", + "express": "^4.19.2" + }, + "devDependencies": { + "tsx": "^4.20.6" + } + }, + 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.../dataIngestion.ts | 32 +- .../dataset.json | 190 ++++------ .../main.ts | 356 ++++++++++++------ .../workflow_graph.png | Bin 26754 -> 29690 bytes 5 files changed, 342 insertions(+), 250 deletions(-) diff --git a/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/README.md b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/README.md index b021ccfd..8e58214e 100644 --- a/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/README.md +++ b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/README.md @@ -1,6 +1,6 @@ # LangGraph + Elasticsearch Human-in-the-Loop -Flight search application using LangGraph for human-in-the-loop workflow and Elasticsearch for vector search. +Flight search application using LangGraph for human-in-the-loop workflow and Elasticsearch for vector search. This is a supporting blog content for the article: [Building Human-in-the-Loop AI Agents with LangGraph and Elasticsearch](https://www.elastic.co/search-labs/blog/human-in-the-loop-with-langgraph-and-elasticsearch). ## Prerequisites @@ -29,8 +29,8 @@ Create a `.env` file in the root directory: ```env ELASTICSEARCH_ENDPOINT=https://your-elasticsearch-instance.com -ELASTICSEARCH_API_KEY=your-api-key -OPENAI_API_KEY=your-openai-api-key +ELASTICSEARCH_API_KEY="your-api-key" +OPENAI_API_KEY="your-openai-api-key" ``` ## Usage @@ -46,11 +46,3 @@ npm start - 👤 Human-in-the-loop workflow with LangGraph - đź“Š Workflow visualization (generates `workflow_graph.png`) -## Workflow - -1. **Retrieve Flights** - Search Elasticsearch with vector similarity -2. **Evaluate Results** - Auto-select if 1 result, show options if multiple -3. **Show Results** - Display flight options to user -4. **Request User Choice** - Pause workflow for user input (HITL) -5. **Disambiguate & Answer** - Use LLM to interpret selection and return final answer - diff --git a/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataIngestion.ts b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataIngestion.ts index 7d4a7939..22f52e5b 100644 --- a/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataIngestion.ts +++ b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataIngestion.ts @@ -6,19 +6,16 @@ import dotenv from "dotenv"; dotenv.config(); -const VECTOR_INDEX = "flights-offerings"; +const VECTOR_INDEX = "legal-precedents"; // Types export interface DocumentMetadata { - from_city: string; - to_city: string; - airport_code: string; - airport_name: string; - country: string; - airline: string; - date: string; - price: number; - time_approx: string; + caseId: string; + contractType: string; + delayPeriod: string; + outcome: string; + reasoning: string; + keyTerms: string; title: string; } @@ -88,20 +85,17 @@ export async function ingestData(): Promise { metadata: { type: "object", properties: { - from_city: { type: "keyword" }, - to_city: { type: "keyword" }, - airport_code: { type: "keyword" }, - airport_name: { + caseId: { type: "keyword" }, + contractType: { type: "keyword" }, + delayPeriod: { type: "text", fields: { keyword: { type: "keyword" }, }, }, - country: { type: "keyword" }, - airline: { type: "keyword" }, - date: { type: "date" }, - price: { type: "integer" }, - time_approx: { type: "keyword" }, + outcome: { type: "keyword" }, + reasoning: { type: "text" }, + keyTerms: { type: "text" }, title: { type: "text", fields: { diff --git a/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataset.json b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataset.json index 46a4b408..ec6a5b0a 100644 --- a/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataset.json +++ b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/dataset.json @@ -1,152 +1,122 @@ [ { - "pageContent": "Ticket: MedellĂ­n (MDE) → Sao Paulo (GRU). 1 stop. Airline: AndeanSky.", + "pageContent": "Legal precedent: Case A - Service delivery delay considered breach. A software development contract stipulated delivery within 'reasonable time'. A two-week delay significantly impacted the client's product launch, causing measurable financial harm. The court ruled this constituted material breach despite no explicit deadline.", "metadata": { - "from_city": "MedellĂ­n", - "to_city": "Sao Paulo", - "airport_code": "GRU", - "airport_name": "Guarulhos International", - "country": "Brazil", - "airline": "AndeanSky", - "date": "2025-10-10", - "price": 480, - "time_approx": "7h 30m", - "title": "MDE → GRU (AndeanSky)" + "caseId": "CASE-A-2023", + "contractType": "service agreement", + "delayPeriod": "two weeks", + "outcome": "breach found", + "reasoning": "delay affected operations and caused financial harm", + "keyTerms": "reasonable time, material breach, timely delivery", + "title": "Case A: Delay Breach with Operational Impact" } }, { - "pageContent": "Ticket: MedellĂ­n (MDE) → Sao Paulo (CGH). 1 stop. Airline: CoffeeAir.", + "pageContent": "Legal precedent: Case B - Service delay not considered breach. A consulting contract used term 'timely delivery' without specific dates. A three-week delay occurred but contract lacked explicit schedule. Court ruled no breach as parties had not defined concrete timeline and delay did not cause demonstrable harm.", "metadata": { - "from_city": "MedellĂ­n", - "to_city": "Sao Paulo", - "airport_code": "CGH", - "airport_name": "Congonhas", - "country": "Brazil", - "airline": "CoffeeAir", - "date": "2025-10-10", - "price": 455, - "time_approx": "8h 05m", - "title": "MDE → CGH (CoffeeAir)" + "caseId": "CASE-B-2022", + "contractType": "consulting agreement", + "delayPeriod": "three weeks", + "outcome": "no breach found", + "reasoning": "no explicit deadline defined, no demonstrable harm", + "keyTerms": "timely delivery, open terms, schedule definition", + "title": "Case B: Delay Without Explicit Schedule" } }, { - "pageContent": "Ticket: MedellĂ­n (MDE) → Tokyo (HND). 2 stops. Airline: CondorJet.", + "pageContent": "Legal precedent: Case C - Delay permitted due to justified causes. A construction service contract had defined timeline but external regulatory approval delays prevented timely completion. Court found delay was justified and did not constitute breach when causes were beyond provider's control.", "metadata": { - "from_city": "MedellĂ­n", - "to_city": "Tokyo", - "airport_code": "HND", - "airport_name": "Haneda", - "country": "Japan", - "airline": "CondorJet", - "date": "2025-11-05", - "price": 1290, - "time_approx": "22h 10m", - "title": "MDE → HND (CondorJet)" + "caseId": "CASE-C-2023", + "contractType": "construction service", + "delayPeriod": "one month", + "outcome": "no breach found", + "reasoning": "external factors beyond control, force majeure applied", + "keyTerms": "justified causes, force majeure, regulatory delays", + "title": "Case C: Justified Delay External Factors" } }, { - "pageContent": "Ticket: MedellĂ­n (MDE) → Tokyo (NRT). 1–2 stops. Airline: CaribeWings.", + "pageContent": "Legal precedent: Case D - Interpretation of 'prompt delivery' term. Contract stated services must be provided 'promptly'. Court analyzed industry standards and prior dealings between parties to determine reasonableness. Ten-day delay was found acceptable given industry norms for similar services.", "metadata": { - "from_city": "MedellĂ­n", - "to_city": "Tokyo", - "airport_code": "NRT", - "airport_name": "Narita", - "country": "Japan", - "airline": "CaribeWings", - "date": "2025-11-05", - "price": 1215, - "time_approx": "23h 30m", - "title": "MDE → NRT (CaribeWings)" + "caseId": "CASE-D-2021", + "contractType": "service agreement", + "delayPeriod": "ten days", + "outcome": "no breach found", + "reasoning": "industry standards applied, reasonable interpretation", + "keyTerms": "prompt delivery, industry standards, reasonable time", + "title": "Case D: Prompt Delivery Industry Standards" } }, { - "pageContent": "Ticket: MedellĂ­n (MDE) → New York (JFK). 1 stop. Airline: AndeanSky.", + "pageContent": "Legal precedent: Case E - Material breach vs minor delay. Service contract had approximate delivery timeline. Five-day delay occurred but service was fully functional and met all quality specifications. Court ruled this was minor breach not justifying contract termination.", "metadata": { - "from_city": "MedellĂ­n", - "to_city": "New York", - "airport_code": "JFK", - "airport_name": "John F. Kennedy International", - "country": "USA", - "airline": "AndeanSky", - "date": "2025-12-01", - "price": 340, - "time_approx": "6h 40m", - "title": "MDE → JFK (AndeanSky)" + "caseId": "CASE-E-2022", + "contractType": "service agreement", + "delayPeriod": "five days", + "outcome": "minor breach only", + "reasoning": "delay minimal, quality maintained, termination unjustified", + "keyTerms": "material breach, substantial performance, termination rights", + "title": "Case E: Minor Delay Quality Maintained" } }, { - "pageContent": "Ticket: MedellĂ­n (MDE) → New York (LGA). 1 stop. Airline: CoffeeAir.", + "pageContent": "Legal precedent: Case F - Open-ended contract terms require good faith. Contract used 'timely manner' without definition. Provider took reasonable steps but faced unexpected technical issues causing delay. Court ruled parties must interpret open terms in good faith and delay was reasonable given circumstances.", "metadata": { - "from_city": "MedellĂ­n", - "to_city": "New York", - "airport_code": "LGA", - "airport_name": "LaGuardia", - "country": "USA", - "airline": "CoffeeAir", - "date": "2025-12-01", - "price": 325, - "time_approx": "6h 55m", - "title": "MDE → LGA (CoffeeAir)" + "caseId": "CASE-F-2023", + "contractType": "technology service", + "delayPeriod": "two weeks", + "outcome": "no breach found", + "reasoning": "good faith interpretation, reasonable efforts demonstrated", + "keyTerms": "timely manner, good faith, open terms interpretation", + "title": "Case F: Good Faith Open Terms" } }, { - "pageContent": "Ticket: MedellĂ­n (MDE) → London (LHR). 1 stop. Airline: CondorJet.", + "pageContent": "Legal precedent: Case G - Schedule linked to milestones. Service contract defined delivery as 'upon completion of client's approval process'. Delay occurred but was directly caused by client's slow approval. Court found provider not in breach as timeline was dependent on client actions.", "metadata": { - "from_city": "MedellĂ­n", - "to_city": "London", - "airport_code": "LHR", - "airport_name": "Heathrow", - "country": "UK", - "airline": "CondorJet", - "date": "2026-01-15", - "price": 890, - "time_approx": "14h 30m", - "title": "MDE → LHR (CondorJet)" + "caseId": "CASE-G-2022", + "contractType": "professional service", + "delayPeriod": "variable", + "outcome": "no breach found", + "reasoning": "timeline dependent on client actions, conditional delivery", + "keyTerms": "milestone delivery, conditional timeline, client obligations", + "title": "Case G: Milestone Dependent Delivery" } }, { - "pageContent": "Ticket: MedellĂ­n (MDE) → London (LGW). 1–2 stops. Airline: CaribeWings.", + "pageContent": "Legal precedent: Case H - Repeated delays pattern. Service provider had history of missing approximate deadlines across multiple deliverables. Court found pattern of delays constituted breach even when individual delays seemed minor, showing failure to meet contractual obligations consistently.", "metadata": { - "from_city": "MedellĂ­n", - "to_city": "London", - "airport_code": "LGW", - "airport_name": "Gatwick", - "country": "UK", - "airline": "CaribeWings", - "date": "2026-01-15", - "price": 760, - "time_approx": "15h 10m", - "title": "MDE → LGW (CaribeWings)" + "caseId": "CASE-H-2021", + "contractType": "ongoing service agreement", + "delayPeriod": "multiple instances", + "outcome": "breach found", + "reasoning": "pattern demonstrated failure to perform, cumulative effect", + "keyTerms": "repeated breach, pattern of conduct, consistent performance", + "title": "Case H: Pattern of Repeated Delays" } }, { - "pageContent": "Ticket: MedellĂ­n (MDE) → Paris (CDG). 1 stop. Airline: CoffeeAir.", + "pageContent": "Legal precedent: Case I - Contractual obligation interpretation. Contract required 'expeditious service delivery'. Court examined contract as whole, prior communications, and purpose of service to interpret meaning. Two-week delay found unreasonable given urgent nature stated in preliminary negotiations.", "metadata": { - "from_city": "MedellĂ­n", - "to_city": "Paris", - "airport_code": "CDG", - "airport_name": "Charles de Gaulle", - "country": "France", - "airline": "CoffeeAir", - "date": "2025-10-22", - "price": 720, - "time_approx": "13h 50m", - "title": "MDE → CDG (CoffeeAir)" + "caseId": "CASE-I-2023", + "contractType": "urgent service agreement", + "delayPeriod": "two weeks", + "outcome": "breach found", + "reasoning": "context and preliminary negotiations showed urgency expectation", + "keyTerms": "expeditious delivery, contract interpretation, preliminary negotiations", + "title": "Case I: Urgent Service Context" } }, { - "pageContent": "Ticket: MedellĂ­n (MDE) → Paris (ORY). 1 stop. Airline: AndeanSky.", + "pageContent": "Legal precedent: Case J - Provider notification obligations. Service contract had flexible timeline but required provider to notify of delays. Provider failed to communicate expected delay. Court found this failure to notify constituted breach regardless of whether delay itself was reasonable.", "metadata": { - "from_city": "MedellĂ­n", - "to_city": "Paris", - "airport_code": "ORY", - "airport_name": "Orly", - "country": "France", - "airline": "AndeanSky", - "date": "2025-10-22", - "price": 695, - "time_approx": "13h 20m", - "title": "MDE → ORY (AndeanSky)" + "caseId": "CASE-J-2022", + "contractType": "service agreement", + "delayPeriod": "one week", + "outcome": "breach found", + "reasoning": "failure to notify violated contractual communication obligations", + "keyTerms": "notification duty, communication obligations, transparency", + "title": "Case J: Notification Failure Breach" } } ] diff --git a/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/main.ts b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/main.ts index 41f3593d..1828b1c0 100644 --- a/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/main.ts +++ b/supporting-blog-content/human-in-the-loop-with-langgraph-and-elasticsearch/main.ts @@ -10,9 +10,9 @@ import { ElasticVectorSearch } from "@langchain/community/vectorstores/elasticse import { Client } from "@elastic/elasticsearch"; import { writeFileSync } from "node:fs"; import readline from "node:readline"; -import { ingestData, Document, DocumentMetadata } from "./dataIngestion.ts"; +import { ingestData, Document } from "./dataIngestion.ts"; -const VECTOR_INDEX = "flights-offerings"; +const VECTOR_INDEX = "legal-precedents"; const llm = new ChatOpenAI({ model: "gpt-4o-mini" }); const embeddings = new OpenAIEmbeddings({ @@ -20,9 +20,9 @@ const embeddings = new OpenAIEmbeddings({ }); const esClient = new Client({ - node: process.env.ELASTICSEARCH_ENDPOINT!, + node: process.env.ELASTICSEARCH_ENDPOINT, auth: { - apiKey: process.env.ELASTICSEARCH_API_KEY!, + apiKey: process.env.ELASTICSEARCH_API_KEY ?? "", }, }); @@ -31,141 +31,274 @@ const vectorStore = new ElasticVectorSearch(embeddings, { indexName: VECTOR_INDEX, }); -// Define the state schema for application workflow -const SupportState = Annotation.Root({ - input: Annotation(), - candidates: Annotation(), - userChoice: Annotation(), - selected: Annotation(), - final: Annotation(), +// Define the state schema for legal research workflow +const LegalResearchState = Annotation.Root({ + query: Annotation(), + precedents: Annotation(), + selectedPrecedent: Annotation(), + draftAnalysis: Annotation(), + ambiguityDetected: Annotation(), + userClarification: Annotation(), + finalAnalysis: Annotation(), }); -// Node 1: Retrieve data from Elasticsearch -async function retrieveFlights(state: typeof SupportState.State) { - const results = await vectorStore.similaritySearch(state.input, 2); - const candidates = results.map((d) => d as Document); - - console.log(`đź“‹ Found ${candidates.length} different flights`); - return { candidates }; -} - -// Node 2: Evaluate if there are 1 or multiple responses -function evaluateResults(state: typeof SupportState.State) { - const candidates = state.candidates || []; - - // If there is 1 result, auto-select it - if (candidates.length === 1) { - const metadata = candidates[0].metadata || {}; - - return { - selected: candidates[0], - final: formatFlightDetails(metadata), - }; +// Node 1: Search for relevant legal precedents +async function searchPrecedents(state: typeof LegalResearchState.State) { + console.log( + "đź“š Searching for relevant legal precedents with query:\n", + state.query + ); + + const results = await vectorStore.similaritySearch(state.query, 5); + const precedents = results.map((d) => d as Document); + + console.log(`Found ${precedents.length} relevant precedents:\n`); + + for (let i = 0; i < precedents.length; i++) { + const p = precedents[i]; + const m = p.metadata; + console.log( + `${i + 1}. ${m.title} (${m.caseId})\n` + + ` Type: ${m.contractType}\n` + + ` Outcome: ${m.outcome}\n` + + ` Key reasoning: ${m.reasoning}\n` + + ` Delay period: ${m.delayPeriod}\n` + ); } - return { candidates }; + return { precedents }; } -// Node 3: Request user choice (separate from showing) -function requestUserChoice() { +// Node 2: HITL #1 - Request lawyer to select most relevant precedent +function precedentSelection(state: typeof LegalResearchState.State) { + console.log("\n⚖️ HITL #1: Human input needed\n"); const userChoice = interrupt({ - question: `Which flight do you prefer?:`, + question: "👨‍⚖️ Which precedent is most similar to your case? ", }); - return { userChoice }; } -// Node 4: Disambiguate user choice and provide final answer -async function disambiguateAndAnswer(state: typeof SupportState.State) { - const candidates = state.candidates || []; - const userInput = state.userChoice || ""; +// Node 3: Process precedent selection +async function selectPrecedent(state: typeof LegalResearchState.State) { + const precedents = state.precedents || []; + const userInput = (state as any).userChoice || ""; + + const precedentsList = precedents + .map((p, i) => { + const m = p.metadata; + return `${i + 1}. ${m.caseId}: ${m.title} - ${m.outcome}`; + }) + .join("\n"); + + const structuredLlm = llm.withStructuredOutput({ + name: "precedent_selection", + schema: { + type: "object", + properties: { + selected_number: { + type: "number", + description: + "The precedent number selected by the lawyer (1-based index)", + minimum: 1, + maximum: precedents.length, + }, + }, + required: ["selected_number"], + }, + }); const prompt = ` - Based on the user's response: "${userInput}" + The lawyer said: "${userInput}" + + Available precedents: + ${precedentsList} + + Which precedent number (1-${precedents.length}) matches their selection? + `; + + const response = await structuredLlm.invoke([ + { + role: "system", + content: + "You are an assistant that interprets lawyer's selection and returns the corresponding precedent number.", + }, + { role: "user", content: prompt }, + ]); + + const selectedIndex = response.selected_number - 1; + const selectedPrecedent = precedents[selectedIndex] || precedents[0]; + + console.log(`âś… Selected: ${selectedPrecedent.metadata.title}\n`); + return { selectedPrecedent }; +} - These are the available flights: - ${candidates - .map( - (d, i) => - `${i}. ${d.metadata?.title} - ${d.metadata?.to_city} (${d.metadata?.airport_code}) - ${d.metadata?.airline} - $${d.metadata?.price} - ${d.metadata?.time_approx}` - ) - .join("\n")} +// Node 4: Draft initial legal analysis +async function createDraft(state: typeof LegalResearchState.State) { + console.log("đź“ť Drafting initial legal analysis...\n"); + + const precedent = state.selectedPrecedent; + if (!precedent) return { draftAnalysis: "" }; + + const m = precedent.metadata; + + const structuredLlm = llm.withStructuredOutput({ + name: "draft_analysis", + schema: { + type: "object", + properties: { + needs_clarification: { + type: "boolean", + description: + "Whether the analysis requires clarification about contract terms or context", + }, + analysis_text: { + type: "string", + description: "The draft legal analysis or the ambiguity explanation", + }, + missing_information: { + type: "array", + items: { type: "string" }, + description: + "List of specific information needed if clarification is required (empty if no clarification needed)", + }, + }, + required: ["needs_clarification", "analysis_text", "missing_information"], + }, + }); - Which flight is the user selecting? Respond ONLY with the flight number (1, 2, or 3). + const prompt = ` + Based on this precedent: + Case: ${m.title} + Outcome: ${m.outcome} + Reasoning: ${m.reasoning} + Key terms: ${m.keyTerms} + + And the lawyer's question: "${state.query}" + + Draft a legal analysis applying this precedent to the question. + + If you need more context about the specific contract terms, timeline details, + or other critical information to provide accurate analysis, set needs_clarification + to true and list what information is missing. + + Otherwise, provide the legal analysis directly. `; - const llmResponse = await llm.invoke([ + const response = await structuredLlm.invoke([ { role: "system", content: - "You are an assistant that interprets user selection. Respond ONLY with the selected flight number.", + "You are a legal research assistant that analyzes cases and identifies when additional context is needed.", }, { role: "user", content: prompt }, ]); - const selectedNumber = Number.parseInt(llmResponse.content as string, 10); // Convert to zero-based index - const selectedFlight = candidates[selectedNumber] ?? candidates[0]; // Fallback to first option + let displayText: string; + if (response.needs_clarification) { + const missingInfoList = response.missing_information + .map((info: string, i: number) => `${i + 1}. ${info}`) + .join("\n"); + displayText = `AMBIGUITY DETECTED:\n${response.analysis_text}\n\nMissing information:\n${missingInfoList}`; + } else { + displayText = `ANALYSIS:\n${response.analysis_text}`; + } + + console.log(displayText + "\n"); return { - selected: selectedFlight, - final: formatFlightDetails(selectedFlight.metadata), + draftAnalysis: displayText, + ambiguityDetected: response.needs_clarification, }; } -// Node 5: Show results only -function showResults(state: typeof SupportState.State) { - const candidates = state.candidates || []; - const formattedOptions = candidates - .map((d: Document, index: number) => { - const m = d.metadata || {}; +// Node 5: HITL #2 - Request clarification from lawyer +function requestClarification(state: typeof LegalResearchState.State) { + console.log("\n⚖️ HITL #2: Additional context needed\n"); + const userClarification = interrupt({ + question: "👨‍⚖️ Please provide clarification about your contract terms:", + }); + return { userClarification }; +} - return `${index + 1}. ${m.title} - ${m.to_city} - ${m.airport_name}(${ - m.airport_code - }) airport - ${m.airline} - $${m.price} - ${m.time_approx}`; - }) - .join("\n"); +// Node 6: Generate final analysis with clarification +async function generateFinalAnalysis(state: typeof LegalResearchState.State) { + console.log("đź“‹ Generating final legal analysis...\n"); - console.log(`\nđź“‹ Flights found:\n${formattedOptions}\n`); + const precedent = state.selectedPrecedent; + if (!precedent) return { finalAnalysis: "" }; - return state; -} + const m = precedent.metadata; -// Helper function to format flight details -function formatFlightDetails(metadata: DocumentMetadata): string { - return `Selected flight: ${metadata.title} - ${metadata.airline} - From: ${metadata.from_city} (${ - metadata.from_city?.slice(0, 3).toUpperCase() || "N/A" - }) - To: ${metadata.to_city} (${metadata.airport_code}) - Airport: ${metadata.airport_name} - Price: $${metadata.price} - Duration: ${metadata.time_approx} - Date: ${metadata.date}`; + const prompt = ` + Original question: "${state.query}" + + Selected precedent: ${m.title} + Outcome: ${m.outcome} + Reasoning: ${m.reasoning} + + Lawyer's clarification: "${state.userClarification}" + + Provide a comprehensive legal analysis integrating: + 1. The selected precedent's reasoning + 2. The lawyer's specific contract context + 3. Conditions for breach vs. no breach + 4. Practical recommendations + `; + + const response = await llm.invoke([ + { + role: "system", + content: + "You are a legal research assistant providing comprehensive analysis.", + }, + { role: "user", content: prompt }, + ]); + + const finalAnalysis = response.content as string; + + console.log( + "\n" + + "=".repeat(80) + + "\n" + + "⚖️ FINAL LEGAL ANALYSIS\n" + + "=".repeat(80) + + "\n\n" + + finalAnalysis + + "\n\n" + + "=".repeat(80) + + "\n" + ); + + return { finalAnalysis }; } -// Build the graph -const workflow = new StateGraph(SupportState) - .addNode("retrieveFlights", retrieveFlights) - .addNode("evaluateResults", evaluateResults) - .addNode("showResults", showResults) - .addNode("requestUserChoice", requestUserChoice) - .addNode("disambiguateAndAnswer", disambiguateAndAnswer) - .addEdge("__start__", "retrieveFlights") - .addEdge("retrieveFlights", "evaluateResults") +// Build the legal research workflow graph +const workflow = new StateGraph(LegalResearchState) + .addNode("searchPrecedents", searchPrecedents) + .addNode("precedentSelection", precedentSelection) + .addNode("selectPrecedent", selectPrecedent) + .addNode("createDraft", createDraft) + .addNode("requestClarification", requestClarification) + .addNode("generateFinalAnalysis", generateFinalAnalysis) + .addEdge("__start__", "searchPrecedents") + .addEdge("searchPrecedents", "precedentSelection") // HITL #1 + .addEdge("precedentSelection", "selectPrecedent") + .addEdge("selectPrecedent", "createDraft") .addConditionalEdges( - "evaluateResults", - (state: typeof SupportState.State) => { - if (state.final) return "complete"; // 0 or 1 result - return "multiple"; // multiple results + "createDraft", + (state: typeof LegalResearchState.State) => { + // If ambiguity detected, request clarification (HITL #2) + if (state.ambiguityDetected) return "needsClarification"; + // Otherwise, generate final analysis + return "final"; }, { - complete: "__end__", - multiple: "showResults", + needsClarification: "requestClarification", + final: "generateFinalAnalysis", } ) - .addEdge("showResults", "requestUserChoice") - .addEdge("requestUserChoice", "disambiguateAndAnswer") - .addEdge("disambiguateAndAnswer", "__end__"); + .addEdge("requestClarification", "generateFinalAnalysis") // HITL #2 + .addEdge("generateFinalAnalysis", "__end__"); /** * Get user input from the command line @@ -208,34 +341,37 @@ async function saveGraphImage(app: any): Promise { * Main execution function */ async function main() { - // Ingest data await ingestData(); // Compile workflow const app = workflow.compile({ checkpointer: new MemorySaver() }); - const config = { configurable: { thread_id: "hitl-thread" } }; + const config = { configurable: { thread_id: "hitl-circular-thread" } }; - // Save graph image await saveGraphImage(app); // Execute workflow - const question = "Flights to Tokyo"; // User query - console.log(`🔍 SEARCHING USER QUESTION: "${question}"\n`); + const legalQuestion = + "Does a pattern of repeated delays constitute breach even if each individual delay is minor?"; + + console.log(`⚖️ LEGAL QUESTION: "${legalQuestion}"\n`); - let currentState = await app.invoke({ input: question }, config); + let currentState = await app.invoke({ query: legalQuestion }, config); - // Handle interruption - if ((currentState as any).__interrupt__?.length > 0) { + // Handle all interruptions in a loop + while ((currentState as any).__interrupt__?.length > 0) { console.log("\nđź’­ APPLICATION PAUSED WAITING FOR USER INPUT..."); - const userChoice = await getUserInput("👤 CHOICE ONE OPTION: "); + + const interruptQuestion = (currentState as any).__interrupt__[0]?.value + ?.question; + const userChoice = await getUserInput( + interruptQuestion || "👤 YOUR CHOICE: " + ); currentState = await app.invoke( new Command({ resume: userChoice }), config ); } - - console.log("\nâś… Final result: \n", currentState.final); } // Run main function diff --git 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