Update TypeScript examples and READMEs (#35)

* update examples and readmes

* update readme

Author: lucasjacks0n

README.md

@@ -21,6 +21,9 @@ const client = await IndexifyClient.createClient();
 
 ## Usage
 
-See the [getting started](https://getindexify.com/getting_started/) guide for examples of how to use the client.
+See the [getting started](https://docs.getindexify.ai/getting_started/) guide on how to use Indexify.
 
 
+## Examples
+
+You can find various examples in our repo [located here](https://github.com/tensorlakeai/indexify-typescript-client/tree/main/examples).

examples/.gitignore

@@ -0,0 +1 @@
+node_modules

examples/README.md

@@ -0,0 +1,25 @@
+# Indexify TypeScript Client Examples
+This directory contains various examples using Indexify Typescript Client
+
+Install dependencies
+```bash
+npm install
+```
+
+#### [OpenAI Rag](./openaiRag.ts)
+Use OpenAI to answer questions about Kevin Durant based on context from Wikipedia in this RAG example.
+```bash
+npm run openai-rag
+```
+
+#### [Langchain Rag](./langchainRag.ts)
+Langchain retriever example for RAG for answering basic question from a piece of content.
+```bash
+npm run langchain-rag
+```
+
+#### [Extracting Content](./extractContent.ts)
+Example extracting content directly from an extractor using Indexify Client
+```bash
+npm run extract-content
+```

examples/extractContent.ts

@@ -1,4 +1,4 @@
-import { IndexifyClient } from "../src";
+import { IndexifyClient } from "getindexify";
 const fs = require("fs");
 
 // Extract wikipedia article directly from client

examples/langchainRag.ts

@@ -5,20 +5,25 @@ import {
 } from "@langchain/core/runnables";
 import { PromptTemplate } from "@langchain/core/prompts";
 import { StringOutputParser } from "@langchain/core/output_parsers";
-import { IndexifyClient } from "getindexify";
+import { ExtractionGraph, IndexifyClient } from "getindexify";
 import { IndexifyRetriever } from "@getindexify/langchain";
 import { formatDocumentsAsString } from "langchain/util/document";
 
 (async () => {
   // setup client
-  const client = await IndexifyClient.createNamespace("testlangchain");
-  client.addExtractionPolicy({
-    extractor: "tensorlake/minilm-l6",
-    name: "minilml6",
+  const graph = ExtractionGraph.fromYaml(`
+  name: 'knowledgebase'
+  extraction_policies:
+    - extractor: 'tensorlake/minilm-l6'
+      name: 'minilml6'
+  `);
+  const client = await IndexifyClient.createNamespace({
+    name: "testlangchain",
+    extractionGraphs: [graph],
   });
 
   // add documents
-  client.addDocuments("Lucas is from Los Angeles, California");
+  await client.addDocuments("knowledgebase", "Lucas is from Los Angeles, California");
 
   await new Promise((r) => setTimeout(r, 5000));
 
@@ -48,7 +53,7 @@ import { formatDocumentsAsString } from "langchain/util/document";
   ]);
 
   const question = "Where is Lucas From?";
-  console.log(`Question: ${question}`)
+  console.log(`Question: ${question}`);
   const result = await chain.invoke(question);
-  console.log(result)
+  console.log(result);
 })();

examples/openaiRag.ts

@@ -0,0 +1,78 @@
+
+import { OpenAI } from "openai";
+import wiki from "wikijs";
+import { IndexifyClient, ExtractionGraph } from "getindexify";
+
+// RAG example with OpenAI and indexify
+(async () => {
+  const client = await IndexifyClient.createClient();
+  const graph = ExtractionGraph.fromYaml(`
+      name: 'nbakb'
+      extraction_policies:
+        - extractor: 'tensorlake/minilm-l6'
+          name: 'wikipediaembedding'
+      `);
+  await client.createExtractionGraph(graph);
+
+  // Function to load wikipedia article from query
+  async function loadWikipediaArticle(query: string) {
+    const page = await wiki().page(query);
+    const wikipediaContent = await page.rawContent();
+    // chunk text
+    const splitText = require("split-text");
+    const chunks = splitText(wikipediaContent, { length: 500 });
+    chunks.forEach(async (chunk: string) => {
+      await client.addDocuments("nbakb", chunk);
+    });
+  }
+
+  // Function to get context
+  // This will search index
+  async function getContext(
+    question: string,
+    index: string,
+    topK: number = 3
+  ): Promise<string> {
+    const results = await client.searchIndex(index, question, topK);
+    let context = "";
+    results.forEach((result) => {
+      context += `content id: ${result.content_id} \n\n passage: ${result.text}\n`;
+    });
+    return context;
+  }
+
+  // Create prompt from question and context
+  function createPrompt(question: string, context: string): string {
+    return `Answer the question, based on the context.\n question: ${question} \n context: ${context}`;
+  }
+
+  // Load wikipedia article
+  await loadWikipediaArticle("kevin durant");
+
+  // Setup OpenAI Client, Prompts and Context
+  const clientOpenAI = new OpenAI();
+  const question = "When and where did Kevin Durant win NBA championships?";
+  const context = await getContext(
+    question,
+    "nbakb.wikipediaembedding.embedding"
+  );
+  const prompt = createPrompt(question, context);
+
+  // Perform rag with prompt
+  clientOpenAI.chat.completions
+    .create({
+      messages: [
+        {
+          role: "user",
+          content: prompt,
+        },
+      ],
+      model: "gpt-3.5-turbo",
+    })
+    .then((chatCompletion) => {
+      console.log(chatCompletion.choices[0].message.content);
+    })
+    .catch((error) => {
+      console.error(error);
+    });
+})();