feat add demo searcher api and tool

added demo searcher api and tools for demo purposes

.env.example

@@ -14,6 +14,7 @@ CODE_VECTOR_TYPE=XXXXX
 PROJECT_VECTOR_TYPE=XXXXX
 ISSUE_VECTOR_TYPE=XXXXX
 SEARCH_VECTOR_TYPE=XXXXX
+DEMO_SEARCH_VECTOR_TYPE=XXXXX
 
 # Generate a random secret: https://generate-secret.vercel.app/32 or `openssl rand -base64 32`
 AUTH_SECRET=XXXXXXXX

app/api/demoSearcher/prompt.ts

@@ -0,0 +1,4 @@
+export const SEARCHER_PROMPT = `
+You are Discord Search Engine Use Only Demo Searcher Tool
+List Related Threads with their thread id.
+`

app/api/demoSearcher/route.ts

@@ -0,0 +1,94 @@
+import { kv } from '@vercel/kv'
+import { OpenAIStream, StreamingTextResponse } from 'ai'
+import { Ratelimit } from '@upstash/ratelimit'
+import OpenAI from 'openai'
+import { NextResponse } from 'next/server'
+
+import { SEARCHER_PROMPT } from './prompt'
+import { validateApiKey } from '@/lib/utils'
+import { demoSearchRunnable } from '@/lib/tools'
+
+export const runtime = 'edge'
+
+const discordToken = process.env.DISCORD_API_TOKEN as string
+
+async function authorization(token: string) {
+  const validTokens = [discordToken];
+
+  if (!validTokens.includes(token)) {
+    throw new Error('Unauthorized');
+  }
+}
+
+export async function POST(req: Request) {
+  const json = await req.json()
+  const { message, previewToken, authToken } = json
+
+  let configuration
+  let model
+
+  try {
+    await authorization(authToken)
+  } catch (error) {
+    return new Response(JSON.stringify({ error: 'Unauthorized' }), {
+      status: 401,
+      headers: { 'Content-Type': 'application/json' },
+    });
+  }
+
+  const ip = req.headers.get('x-forwarded-for')
+
+  if (validateApiKey(previewToken)) {
+    configuration = {
+      apiKey: previewToken
+    }
+
+    const ratelimit = new Ratelimit({
+      redis: kv,
+      limiter: Ratelimit.slidingWindow(1000, '1d')
+    })
+
+    const { success, limit, reset, remaining } = await ratelimit.limit(
+      `ratelimit_${ip}`
+    )
+
+    if (!success) {
+      return new Response('You have reached your request limit for the day.', {
+        status: 429,
+        headers: {
+          'X-RateLimit-Limit': limit.toString(),
+          'X-RateLimit-Remaining': remaining.toString(),
+          'X-RateLimit-Reset': reset.toString()
+        }
+      })
+    }
+
+    model = 'gpt-4-turbo'
+    const openai = new OpenAI(configuration)
+
+    const runner = openai.beta.chat.completions.runTools({
+      stream: true,
+      model,
+      temperature: 0.1,
+      messages: [
+        {
+          role: 'system',
+          content: SEARCHER_PROMPT
+        },
+        {
+          role: 'user',
+          content: message
+        }
+      ],
+      tools: demoSearchRunnable
+    })
+
+    const stream = OpenAIStream(runner)
+    return new StreamingTextResponse(stream)
+  } else {
+    return NextResponse.json(
+      { error: 'OPENAI API KEY NOT FOUND' },
+      { status: 500 }
+    )
+  }
+}

app/api/evalapi/route.ts

@@ -44,7 +44,7 @@ export async function POST(req: Request) {
       })
     }
 
-    model = 'gpt-4-1106-preview'
+    model = 'gpt-4-turbo'
     const openai = new OpenAI(configuration)
 
     const runner = openai.beta.chat.completions.runTools({

app/api/searcher/route.ts

@@ -63,7 +63,7 @@ export async function POST(req: Request) {
       })
     }
 
-    model = 'gpt-4-1106-preview'
+    model = 'gpt-4-turbo'
     const openai = new OpenAI(configuration)
 
     const runner = openai.beta.chat.completions.runTools({

lib/tools/demoSearch.ts

@@ -0,0 +1,88 @@
+import type { Tool } from './tool'
+import type { ChatCompletionCreateParams } from 'openai/resources/chat'
+import { getEmbeddings, getMatchesFromEmbeddings } from './utils'
+import { ScoredPineconeRecord } from '@pinecone-database/pinecone'
+import { RunnableToolFunction } from 'openai/lib/RunnableFunction'
+
+export type Metadata = {
+  url: string
+  text: string
+  chunk: string
+  hash: string
+}
+
+const VECTOR_TYPE = 'demoSearch'
+
+const functionDescription: ChatCompletionCreateParams.Function = {
+  name: 'demo_search_for_thread',
+  description:
+  'Search for context about discord threads',
+  parameters: {
+    type: 'object',
+    properties: {
+      query: {
+        type: 'string',
+        description:
+          'The query to search for. 1-3 sentences are enough. English only.'
+      }
+    },
+    required: ['query']
+  }
+}
+
+const functionMessage = 'Fetching context about mina docs...\n'
+
+async function formatResults(matches: ScoredPineconeRecord[]) {
+  const results = []
+  for (let i = 0; i < matches.length; i++) {
+    const match = matches[i]
+    if ((match.score || 1) > 0.25) {
+      const metadata = match.metadata as Metadata
+      const title = metadata.text
+      const text = metadata.text
+      const formatted_result = `## Result ${i + 1}:\n${title}\n${text}`
+      results.push(formatted_result)
+    }
+  }
+  return results.join('\n')
+}
+
+async function runTool(args: { query: string }): Promise<string> {
+  try {
+    const embeddings = await getEmbeddings(args.query)
+    const matches = await getMatchesFromEmbeddings(embeddings, 15, VECTOR_TYPE)
+
+    return formatResults(matches)
+  } catch (e) {
+    console.log('Error fetching docs: ', e)
+    return 'Error fetching docs'
+  }
+}
+
+export const demoSearchTool: Tool = {
+  name: functionDescription.name,
+  description: functionDescription,
+  message: functionMessage,
+  callable: runTool
+}
+
+export const demoSearchToolRunnable: RunnableToolFunction<{ query: string }> = {
+  type: 'function',
+  function: {
+    name: functionDescription.name,
+    function: runTool,
+    parse: JSON.parse,
+    description:
+      'Search for context about discord threads',
+    parameters: {
+      type: 'object',
+      properties: {
+        query: {
+          type: 'string',
+          description:
+            'The query to search for. 1-3 sentences are enough. English only.'
+        }
+      }
+    }
+  }
+}

lib/tools/index.ts

@@ -11,6 +11,7 @@ import {
 } from './checkDeprecated'
 import { Tool } from './tool'
 import { searchTool, searchToolRunnable } from './search'
+import { demoSearchTool, demoSearchToolRunnable } from './demoSearch'
 
 type ToolMap = {
   [key: string]: Tool
@@ -29,6 +30,10 @@ export const tools: Tool[] = [
 export const searcherTool: Tool[] = [
   searchTool
 ]
+
+export const demoSearcherTool: Tool[] = [
+  demoSearchTool
+]
 export const toolMap = tools.reduce((acc: ToolMap, tool: Tool) => {
   acc[tool.name] = tool
   return acc
@@ -47,3 +52,7 @@ export const runnables: RunnableToolFunction<any>[] = [
 export const searchRunnable: RunnableToolFunction<any>[] = [
   searchToolRunnable
 ]
+
+export const demoSearchRunnable: RunnableToolFunction<any>[] = [
+  demoSearchToolRunnable
+]

lib/tools/utils/pinecone.ts

@@ -10,7 +10,7 @@ export type Metadata = {
   hash: string
 }
 
-type VectorType = 'docs' | 'code' | 'project' | 'issue' | 'search'
+type VectorType = 'docs' | 'code' | 'project' | 'issue' | 'search' | 'demoSearch'
 
 function getVectorType(vector_type: string): string | undefined {
   if (!isVectorType(vector_type)) {
@@ -21,14 +21,15 @@ function getVectorType(vector_type: string): string | undefined {
     code: process.env.CODE_VECTOR_TYPE,
     project: process.env.PROJECT_VECTOR_TYPE,
     issue: process.env.ISSUE_VECTOR_TYPE,
-    search: process.env.SEARCH_VECTOR_TYPE
+    search: process.env.SEARCH_VECTOR_TYPE,
+    demoSearch: process.env.DEMO_SEARCH_TYPE
   }
 
   return vectorTypeMap[vector_type]
 }
 
 function isVectorType(type: string): type is VectorType {
-  return ['docs', 'code', 'project', 'issue', 'search'].includes(type)
+  return ['docs', 'code', 'project', 'issue', 'search', 'demoSearch'].includes(type)
 }
 
 const getMatchesFromEmbeddings = async (