refactor

app/api/chat/route.ts

@@ -2,9 +2,11 @@ import { Configuration, OpenAIApi } from "openai-edge";
 import { OpenAIStream, StreamingTextResponse } from "ai";
 import { createUrlSurfer } from "@/app/tools/surfer";
 import { createSearchApi } from "@/app/tools/search";
+import { createOddsApi } from "@/app/tools/odds";
 
 const [, urlSurferSchema] = createUrlSurfer();
 const [, serpApiSchema] = createSearchApi({ apiKey: process.env.SERP_API_KEY || '' });
+const [, oddsApiSchema] = createOddsApi({ apiKey: process.env.ODDS_API_KEY || '' });
 
 const config = new Configuration({
   apiKey: process.env.OPENAI_API_KEY,
@@ -13,7 +15,8 @@ const openai = new OpenAIApi(config);
 
 const functions: any[] = [
   urlSurferSchema,
-  serpApiSchema
+  serpApiSchema,
+  oddsApiSchema
 ];
 
 export async function POST(req: Request) {

app/tools/odds.ts

@@ -0,0 +1,48 @@
+import { Tool } from 'openai-function-calling-tools';
+import { z } from 'zod';
+
+function createOddsApi({ apiKey }: { apiKey: string }) {
+  const paramsSchema = z.object({
+    input: z.string(),
+  });
+  const name = 'oddsApi';
+  const description = 'A realtime Sports Odds API. Useful for when you need to answer questions about sports odds, currently NBA and NFL. Input should be a sport and a corresponding game. Outputs a JSON array of results.';
+
+  const execute = async ({ input }: z.infer<typeof paramsSchema>) => {
+    try {
+        const oddsFormat = 'american';
+        const dateFormat = 'iso';
+        const regions = 'us';
+        let sportKey;
+
+        let market;
+
+        // americanfootball_nfl_super_bowl_winner
+        if (input.includes('nba')) {
+            sportKey = 'basketball_nba';
+        } else if (input.includes('nfl')) {
+            sportKey = 'americanfootball_nfl';
+        } else {
+            sportKey = 'upcoming';
+        }
+
+        if (input.includes('spread')) {
+            market = 'spread';
+        } else if (input.includes('o/u')) {
+            market = 'totals';
+        } else {
+            market = 'h2h';
+        }
+        const activeSports = await fetch(`https://api.the-odds-api.com/v4/sports/${sportKey}/odds?apiKey=${apiKey}&oddsFormat=${oddsFormat}&dateFormat=${dateFormat}&market=${market}&regions=${regions}`);
+        const oddsResponse = await activeSports.json();
+        console.log(oddsResponse);
+        return JSON.stringify(oddsResponse);
+    } catch (error) {
+      throw new Error(`Error in oddsApi: ${error}`);
+    }
+  };
+
+  return new Tool(paramsSchema, name, description, execute).tool;
+}
+
+export { createOddsApi };

package-lock.json

@@ -30,6 +30,7 @@
         "react-markdown": "^9.0.0",
         "serpapi": "^2.0.0",
         "sonner": "^1.1.0",
+        "the-odds-api": "^2.1.0",
         "zod": "^3.22.4",
         "zod-to-json-schema": "^3.21.4"
       },
@@ -7792,6 +7793,52 @@
       "integrity": "sha512-N+8UisAXDGk8PFXP4HAzVR9nbfmVJ3zYLAWiTIoqC5v5isinhr+r5uaO8+7r3BMfuNIufIsA7RdpVgacC2cSpw==",
       "dev": true
     },
+    "node_modules/the-odds-api": {
+      "version": "2.1.0",
+      "resolved": "https://registry.npmjs.org/the-odds-api/-/the-odds-api-2.1.0.tgz",
+      "integrity": "sha512-Xil75sSw/WJSD4Af5314AQKw7KFNIEiU9NQxVRYbouDzUmCy2HbaL/6PLRP3ExqYx6xp/3D3vL7rmHHXqHYLPw==",
+      "dependencies": {
+        "node-fetch": "^2.3.0"
+      }
+    },
+    "node_modules/the-odds-api/node_modules/node-fetch": {
+      "version": "2.7.0",
+      "resolved": "https://registry.npmjs.org/node-fetch/-/node-fetch-2.7.0.tgz",
+      "integrity": "sha512-c4FRfUm/dbcWZ7U+1Wq0AwCyFL+3nt2bEw05wfxSz+DWpWsitgmSgYmy2dQdWyKC1694ELPqMs/YzUSNozLt8A==",
+      "dependencies": {
+        "whatwg-url": "^5.0.0"
+      },
+      "engines": {
+        "node": "4.x || >=6.0.0"
+      },
+      "peerDependencies": {
+        "encoding": "^0.1.0"
+      },
+      "peerDependenciesMeta": {
+        "encoding": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/the-odds-api/node_modules/tr46": {
+      "version": "0.0.3",
+      "resolved": "https://registry.npmjs.org/tr46/-/tr46-0.0.3.tgz",
+      "integrity": "sha512-N3WMsuqV66lT30CrXNbEjx4GEwlow3v6rr4mCcv6prnfwhS01rkgyFdjPNBYd9br7LpXV1+Emh01fHnq2Gdgrw=="
+    },
+    "node_modules/the-odds-api/node_modules/webidl-conversions": {
+      "version": "3.0.1",
+      "resolved": "https://registry.npmjs.org/webidl-conversions/-/webidl-conversions-3.0.1.tgz",
+      "integrity": "sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ=="
+    },
+    "node_modules/the-odds-api/node_modules/whatwg-url": {
+      "version": "5.0.0",
+      "resolved": "https://registry.npmjs.org/whatwg-url/-/whatwg-url-5.0.0.tgz",
+      "integrity": "sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw==",
+      "dependencies": {
+        "tr46": "~0.0.3",
+        "webidl-conversions": "^3.0.0"
+      }
+    },
     "node_modules/through": {
       "version": "2.3.8",
       "resolved": "https://registry.npmjs.org/through/-/through-2.3.8.tgz",

package.json

@@ -31,6 +31,7 @@
     "react-markdown": "^9.0.0",
     "serpapi": "^2.0.0",
     "sonner": "^1.1.0",
+    "the-odds-api": "^2.1.0",
     "zod": "^3.22.4",
     "zod-to-json-schema": "^3.21.4"
   },

pages/api/functions/embed.ts

@@ -0,0 +1,42 @@
+import { createSearchApi } from '../../../app/tools/search';
+import { createOddsApi } from '@/app/tools/odds';
+import { handleContentText } from './utils';
+import { similaritySearch } from './vector-store';
+
+const urlRegex = /(https?:\/\/[^\s]+)/g;
+
+const [serpApi] =
+  createSearchApi({
+    apiKey: process.env.SERP_API_KEY || "",
+});
+
+const [oddsApi] = createOddsApi({ apiKey: process.env.ODDS_API_KEY || "" });
+type FunctionOutput = any;
+type FunctionInput = any;
+
+export const odds: FunctionOutput = async ({ input }: FunctionInput) => {
+  const content = await oddsApi({input});
+  const oddsApiResults = await similaritySearch(input, content);
+  return oddsApiResults;
+}
+
+export const surfer: FunctionOutput = async ({ input }: FunctionInput) => {
+  const urls = input.match(urlRegex);
+  const targetUrl = urls ? urls[0] : null;
+  const promptWithoutUrl = urls ? input.replace(urlRegex, '').trim() : input;
+
+  const content: string = await handleContentText(targetUrl)
+  if (!content) {
+    return `Couldn't find ${targetUrl}, here is the prompt: ${promptWithoutUrl}`;
+  }
+
+  const surferApiResults = await similaritySearch(promptWithoutUrl, content);
+  return surferApiResults;
+}
+
+export const serp: FunctionOutput = async ({ input }: FunctionInput) => {
+  const content: string = await serpApi({input})
+  const serpApiResults = await similaritySearch(input, content);
+  return serpApiResults;
+}
+

pages/api/functions/index.ts

@@ -1,13 +1,5 @@
 import { NextApiRequest, NextApiResponse } from 'next';
-import fetch, { RequestInfo } from 'node-fetch';
-import { JSDOM } from 'jsdom';
-// @ts-ignore
-import pdfParse from 'pdf-parse';
-import puppeteer from 'puppeteer';
-import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
-import { MemoryVectorStore } from 'langchain/vectorstores/memory';
-import { HuggingFaceTransformersEmbeddings } from "langchain/embeddings/hf_transformers";
-import { createSearchApi } from '../../../app/tools/search'
+import { odds, serp, surfer } from './embed';
 
 export const config = {
     api: {
@@ -17,95 +9,12 @@ export const config = {
     },
 };
 
-const DEFAULT_CHUNK_SIZE = 1000;
-const VECTOR_STORE_SIZE = 10;
-const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: DEFAULT_CHUNK_SIZE });
+type FunctionHandler = any;
 
-async function extractTextFromPDF(buffer: Buffer): Promise<string> {
-  const data = await pdfParse(buffer);
-  return data.text;
-}
-
-const model = new HuggingFaceTransformersEmbeddings({
-    modelName: "Xenova/all-MiniLM-L6-v2",
-});
-
-const urlRegex = /(https?:\/\/[^\s]+)/g;
-
-const [serpApi] =
-  createSearchApi({
-    apiKey: process.env.SERP_API_KEY || "",
-});
-
-const handleContentText = async (targetUrl: string) => {
-  const response = await fetch(targetUrl);
-  const status = response.status;
-  const contentType = response.headers.get('content-type') || '';
-  let content;
-
-  if (status >= 400) {
-    // If status is 400 or greater, try using puppeteer
-    const browser = await puppeteer.launch();
-    const page = await browser.newPage();
-    await page.goto(targetUrl, { waitUntil: 'networkidle0' }); // waits for the network to be idle before considering the navigation to be finished.
-    content = await page.evaluate(() => document.body.innerText);
-    await browser.close();
-    return content;
-  } else if (contentType.includes('application/pdf')) {
-    const buffer = await response.arrayBuffer();
-    content = await extractTextFromPDF(buffer as any);
-  } else if (contentType.includes('text/html')) {
-    const html = await response.text();
-    const dom = new JSDOM(html);
-    const scripts = dom.window.document.querySelectorAll('script, style');
-    scripts.forEach(element => element.remove());
-    content = dom.window.document.body.textContent || '';
-  } else {
-    content = await response.text();
-  }
-  return content.trim();
-}
-
-
-const surferEmbedApi = async ({ input }: any) => {
-  const urls = input.match(urlRegex);
-  const targetUrl = urls ? urls[0] : null;
-  const promptWithoutUrl = urls ? input.replace(urlRegex, '').trim() : input;
-
-  const content: string = await handleContentText(targetUrl)
-  if (!content) {
-    return `Couldn't find ${targetUrl}, here is the prompt: ${promptWithoutUrl}`;
-  }
-  
-  const documents = await textSplitter.createDocuments([content]);
-
-  const vectorStore = await MemoryVectorStore.fromTexts(
-    // @ts-ignore
-    [...documents.map(doc => doc.pageContent)],
-    // @ts-ignore
-    [...documents.map((v, k) => k)],
-    model
-  )
-  const queryResult = await vectorStore.similaritySearch(promptWithoutUrl, VECTOR_STORE_SIZE);
-  return `Here is the context: ${JSON.stringify(queryResult.map(result => result.pageContent))} from using the prompt to lookup relevant information. Here is the prompt: ${promptWithoutUrl}`;
-}
-
-const serpEmbedApi = async ({ input }: any) => {
-  const content: string = await serpApi({input})
-  const documents = await textSplitter.createDocuments([content]);
-  const vectorStore = await MemoryVectorStore.fromTexts(
-    // @ts-ignore
-    [...documents.map(doc => doc.pageContent)],
-    // @ts-ignore
-    [...documents.map((v, k) => k)],
-    model
-  )
-  const queryResult = await vectorStore.similaritySearch(input, VECTOR_STORE_SIZE);
-  return queryResult;
-}
-const handlers: any = {
-  'searchApi': serpEmbedApi,
-  'surfer': surferEmbedApi
+const handlers: FunctionHandler = {
+  'searchApi': serp,
+  'surfer': surfer,
+  'oddsApi': odds
 };
 
 export default async function handler(req: NextApiRequest, res: NextApiResponse) {

pages/api/functions/utils.ts

@@ -0,0 +1,40 @@
+import fetch from 'node-fetch';
+import { JSDOM } from 'jsdom';
+import pdfParse from 'pdf-parse';
+import puppeteer from 'puppeteer';
+
+
+export const extractTextFromPDF = async (buffer: Buffer): Promise<string> => {
+    const data = await pdfParse(buffer);
+    return data.text;
+}
+
+export const handleContentText = async (targetUrl: string) => {
+    const response = await fetch(targetUrl);
+    const status = response.status;
+    const contentType = response.headers.get('content-type') || '';
+    let content;
+  
+    if (status >= 400) {
+      // If status is 400 or greater, try using puppeteer
+      const browser = await puppeteer.launch();
+      const page = await browser.newPage();
+      await page.goto(targetUrl, { waitUntil: 'networkidle0' }); // waits for the network to be idle before considering the navigation to be finished.
+      content = await page.evaluate(() => document.body.innerText);
+      await browser.close();
+      return content;
+    } else if (contentType.includes('application/pdf')) {
+      const buffer = await response.arrayBuffer();
+      content = await extractTextFromPDF(buffer as any);
+    } else if (contentType.includes('text/html')) {
+      const html = await response.text();
+      const dom = new JSDOM(html);
+      const scripts = dom.window.document.querySelectorAll('script, style');
+      scripts.forEach(element => element.remove());
+      content = dom.window.document.body.textContent || '';
+    } else {
+      content = await response.text();
+    }
+    return content.trim();
+}
+  

pages/api/functions/vector-store.ts

@@ -0,0 +1,23 @@
+import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
+import { MemoryVectorStore } from 'langchain/vectorstores/memory';
+import { HuggingFaceTransformersEmbeddings } from "langchain/embeddings/hf_transformers";
+
+const DEFAULT_CHUNK_SIZE = 1000;
+const VECTOR_STORE_SIZE = 3;
+const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: DEFAULT_CHUNK_SIZE });
+
+const model = new HuggingFaceTransformersEmbeddings({
+    modelName: "Xenova/all-MiniLM-L6-v2",
+});
+
+export const similaritySearch = async (input: string, content: string) => {
+    const documents = await textSplitter.createDocuments([content]);
+  
+    const vectorStore = await MemoryVectorStore.fromTexts(    
+      [...documents.map(doc => doc.pageContent)],
+      [...documents.map((v, k) => k)],
+      model
+    )
+    const queryResult = await vectorStore.similaritySearch(input, VECTOR_STORE_SIZE);
+    return queryResult;
+}