[QUO-2017] Polling for detections, cleanup artificats, readme, lint

.eslintrc.json

@@ -1,10 +1,7 @@
 {
   "parser": "@typescript-eslint/parser",
-  "plugins": ["@typescript-eslint"],
-  "extends": [
-    "eslint:recommended",
-    "plugin:@typescript-eslint/recommended"
-  ],
+  "plugins": ["@typescript-eslint", "prettier"],
+  "extends": ["eslint:recommended", "plugin:@typescript-eslint/recommended", "prettier"],
   "env": {
     "node": true,
     "es2022": true
@@ -16,6 +13,7 @@
   "rules": {
     "@typescript-eslint/explicit-function-return-type": "off",
     "@typescript-eslint/no-explicit-any": "off",
-    "@typescript-eslint/no-unused-vars": ["error", { "argsIgnorePattern": "^_" }]
+    "@typescript-eslint/no-unused-vars": ["error", { "argsIgnorePattern": "^_" }],
+    "prettier/prettier": "error"
   }
-} 
+}

.husky/pre-commit

@@ -0,0 +1,4 @@
+#!/usr/bin/env sh
+. "$(dirname -- "$0")/_/husky.sh"
+
+npx lint-staged

.prettierignore

@@ -0,0 +1,5 @@
+dist
+node_modules
+build
+coverage
+package-lock.json 

.prettierrc

@@ -0,0 +1,8 @@
+{
+  "semi": true,
+  "trailingComma": "es5",
+  "singleQuote": true,
+  "printWidth": 100,
+  "tabWidth": 2,
+  "endOfLine": "auto"
+} 

README.md

@@ -1,9 +1,10 @@
 # quotientai
+
 [![npm version](https://img.shields.io/npm/v/quotientai)](https://www.npmjs.com/package/quotientai)
 
 ## Overview
 
-`quotientai` is an SDK built to manage artifacts (prompts, datasets), and run evaluations on [Quotient](https://quotientai.co).
+`quotientai` is an SDK and CLI for logging data to [Quotient](https://quotientai.co), and running hallucination and document attribution detections for retrieval and search-augmented AI systems.
 
 ## Installation
 
@@ -13,13 +14,36 @@ npm install quotientai
 
 ## Usage
 
-Create an API key on Quotient and set it as an environment variable called `QUOTIENT_API_KEY`. Then check out the examples in the `examples/` directory or see our [docs](https://docs.quotientai.co) for a more comprehensive walkthrough.
+Create an API key on [Quotient](https://app.quotientai.co) and set it as an environment variable called `QUOTIENT_API_KEY`. Then follow the examples below or see our [docs](https://docs.quotientai.co) for a more comprehensive walkthrough.
+
+Send your first log and detect hallucinations. Run the code below and see your Logs and Detections on your [Quotient Dashboard](https://app.quotientai.co/dashboard).
 
-The examples directory contains:
-- `example_evaluate.ts` - Running an evaluation against a dataset
-- `example_logs.ts` - Logging with hallucination detection
+```typescript
+import { QuotientAI } from 'quotientai';
+
+const quotient = new QuotientAI(apiKey?: string);
+
+// initialize the logger
+const quotientLogger = quotient.logger.init({
+  appName: 'my-app',
+  environment: 'dev',
+  sampleRate: 1.0,
+  hallucinationDetection: true,
+  hallucination_detection_sample_rate: 1.0,
+});
+
+// create a log
+const logId = await quotientLogger.log({
+  userQuery: 'How do I cook a goose?',
+  modelOutput: 'The capital of France is Paris',
+  documents: ['Here is an excellent goose recipe...'],
+});
+
+// optionally, you can poll for detection results for further actions
+const detectionResults = await quotientLogger.pollForDetections(logId);
+```
 
-### QuotientAI
+### QuotientAI Client
 
 The main client class that provides access to all QuotientAI resources.
 
@@ -31,75 +55,6 @@ new QuotientAI(apiKey?: string)
 
 - `apiKey`: Optional API key. If not provided, will attempt to read from `QUOTIENT_API_KEY` environment variable.
 
-#### Methods
-
-##### evaluate
-
-```typescript
-evaluate(params: {
-  prompt: Prompt;
-  dataset: Dataset;
-  model: Model;
-  parameters: Record<string, any>;
-  metrics: string[];
-}): Promise<Run>
-```
-
-Evaluates a model on a dataset using a prompt.
-
-### QuotientLogger
-
-A logger interface for tracking model interactions.
-
-#### Methods
-
-##### init
-
-```typescript
-init(config: {
-  app_name: string;
-  environment: string;
-  tags?: Record<string, any>;
-  sample_rate?: number;
-  hallucination_detection?: boolean;
-  inconsistency_detection?: boolean;
-  hallucination_detection_sample_rate?: number;
-}): QuotientLogger
-```
-
-Configures the logger with the provided parameters.
-
-##### log
-
-```typescript
-log(params: {
-  user_query: string;
-  model_output: string;
-  documents?: string[];
-  message_history?: Array<Record<string, any>>;
-  instructions?: string[];
-  tags?: Record<string, any>;
-  hallucination_detection?: boolean;
-  inconsistency_detection?: boolean;
-}): Promise<void>
-```
-
-Logs a model interaction.
-
-## Error Handling
-
-The client uses a custom `QuotientAIError` class for error handling:
-
-```typescript
-try {
-  await client.evaluate({ ... });
-} catch (error) {
-  if (error instanceof QuotientAIError) {
-    console.error(`Error ${error.status}: ${error.message}`);
-  }
-}
-```
-
-## License
+## Docs
 
-MIT 
+For comprehensive documentation, please visit our [docs](https://docs.quotientai.co).

examples/example_logs.ts

@@ -1,49 +1,54 @@
-import { QuotientAI } from '../quotientai';
+import { QuotientAI } from 'quotientai';
 
 async function main() {
-    const quotient = new QuotientAI();
-    console.log("QuotientAI client initialized")
+  const quotient = new QuotientAI();
+  console.log('QuotientAI client initialized');
 
-    // configure the logger
-    const quotient_logger = quotient.logger.init({
-        app_name: "my-app",
-        environment: "dev",
-        sample_rate: 1.0,
-        tags: { model: "gpt-4o", feature: "customer-support" },
-        hallucination_detection: true,
-    })
+  // configure the logger
+  const quotientLogger = quotient.logger.init({
+    appName: 'my-app',
+    environment: 'dev',
+    sampleRate: 1.0,
+    tags: { model: 'gpt-4o', feature: 'customer-support' },
+    hallucinationDetection: true,
+    hallucinationDetectionSampleRate: 1.0,
+  });
 
-    console.log("Logger initialized")
+  console.log('Logger initialized');
 
-    // mock retrieved documents
-    const retrieved_documents = [
-        "Sample document 1",
-        {"page_content": "Sample document 2", "metadata": {"source": "website.com"}},
-        {"page_content": "Sample document 3"}
-    ]
+  // mock retrieved documents
+  const retrievedDocuments = [
+    'Sample document 1',
+    { pageContent: 'Sample document 2', metadata: { source: 'website.com' } },
+    { pageContent: 'Sample document 3' },
+  ];
 
-    console.log("Preparing to log with quotient_logger")
-    try {
-        const response = await quotient_logger.log({
-            user_query: "How do I cook a goose?",
-            model_output: "The capital of France is Paris",
-            documents: retrieved_documents,
-            message_history: [
-                {"role": "system", "content": "You are an expert on geography."},
-                {"role": "user", "content": "What is the capital of France?"},
-                {"role": "assistant", "content": "The capital of France is Paris"},
-            ],
-            instructions: [
-                "You are a helpful assistant that answers questions about the world.",
-                "Answer the question in a concise manner. If you are not sure, say 'I don't know'.",
-            ],
-            hallucination_detection: true,
-            inconsistency_detection: true,
-        });
-        console.log(response.message)
-    } catch (error) {
-        console.error(error)
-    }
+  console.log('Preparing to log with quotient_logger');
+  try {
+    const logId = await quotientLogger.log({
+      userQuery: 'How do I cook a test?',
+      modelOutput: 'The capital of France is Paris',
+      documents: retrievedDocuments,
+      messageHistory: [
+        { role: 'system', content: 'You are an expert on geography.' },
+        { role: 'user', content: 'What is the capital of France?' },
+        { role: 'assistant', content: 'The capital of France is Paris' },
+      ],
+      instructions: [
+        'You are a helpful assistant that answers questions about the world.',
+        "Answer the question in a concise manner. If you are not sure, say 'I don't know'.",
+      ],
+      hallucinationDetection: true,
+      inconsistencyDetection: true,
+    });
+    console.log('pollForDetections with logId: ', logId);
+
+    // poll for the detection results
+    const detectionResults = await quotientLogger.pollForDetections(logId);
+    console.log('detectionResults', detectionResults);
+  } catch (error) {
+    console.error(error);
+  }
 }
 
 main().catch(console.error);