Examples

Welcome to the CG SDK examples! Here you'll find practical examples for common use cases.

Available Examples

Chat & Completions

• Basic Chat - Simple chat completions
• Streaming Chat - Real-time streaming responses
• Function Calling - Using tools and function calls

Embeddings & Vectors

• Create Embeddings - Generate text embeddings
• RAG System - Complete RAG implementation
• Semantic Search - Build semantic search

Files & Documents

• File Upload - Upload and process files
• Markdown Conversion - Convert documents to markdown
• Document Q&A - Ask questions about documents

Agents & Tracing

• Agent Tracing - Monitor agent execution
• Multi-Agent System - Coordinate multiple agents

Quick Start Examples

Chat Completion

import { CGateClient } from '@cognipeer/cgate-sdk';

const client = new CGateClient({
  apiKey: process.env.CGATE_API_KEY!,
});

const response = await client.chat.completions.create({
  model: 'gpt-4',
  messages: [
    { role: 'user', content: 'Hello!' }
  ],
});

console.log(response.choices[0].message.content);

Streaming Chat

const stream = await client.chat.completions.create({
  model: 'gpt-4',
  messages: [{ role: 'user', content: 'Tell me a story' }],
  stream: true,
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

RAG System

// 1. Create embeddings for query
const embeddingResponse = await client.embeddings.create({
  model: 'text-embedding-3-small',
  input: 'What is machine learning?',
});

// 2. Search vector database
const results = await client.vectors.query('my-provider', 'knowledge', {
  query: {
    vector: embeddingResponse.data[0].embedding,
    topK: 5,
  },
});

// 3. Generate response with context
const context = results.result.matches
  .map(m => m.metadata?.text)
  .join('\n\n');

const response = await client.chat.completions.create({
  model: 'gpt-4',
  messages: [
    {
      role: 'system',
      content: `Context:\n${context}`,
    },
    {
      role: 'user',
      content: 'What is machine learning?',
    },
  ],
});

Running Examples

Clone the repository and run examples:

git clone https://github.com/Cognipeer/cgate-sdk
cd cgate-sdk/examples

# Install dependencies
npm install

# Set your API key
export CGATE_API_KEY=your-api-key

# Run an example
npm run example:chat
npm run example:rag
npm run example:streaming

Example Projects

Chatbot

Build a conversational AI chatbot:

class Chatbot {
  private client: CGateClient;
  private messages: ChatMessage[] = [];

  constructor(apiKey: string) {
    this.client = new CGateClient({ apiKey });
    this.messages.push({
      role: 'system',
      content: 'You are a helpful assistant.',
    });
  }

  async chat(userMessage: string): Promise<string> {
    this.messages.push({
      role: 'user',
      content: userMessage,
    });

    const response = await this.client.chat.completions.create({
      model: 'gpt-4',
      messages: this.messages,
    });

    const assistantMessage = response.choices[0].message;
    this.messages.push(assistantMessage);

    return assistantMessage.content;
  }
}

const bot = new Chatbot(process.env.CGATE_API_KEY!);
console.log(await bot.chat('Hello!'));
console.log(await bot.chat('What can you help me with?'));

Knowledge Base Search

class KnowledgeBase {
  constructor(
    private client: CGateClient,
    private providerKey: string,
    private indexId: string
  ) {}

  async addDocument(id: string, text: string, metadata?: Record<string, unknown>) {
    const embedding = await this.client.embeddings.create({
      model: 'text-embedding-3-small',
      input: text,
    });

    await this.client.vectors.upsert(this.providerKey, this.indexId, {
      vectors: [
        {
          id,
          values: embedding.data[0].embedding,
          metadata: { text, ...metadata },
        },
      ],
    });
  }

  async search(query: string, topK = 5) {
    const embedding = await this.client.embeddings.create({
      model: 'text-embedding-3-small',
      input: query,
    });

    const results = await this.client.vectors.query(
      this.providerKey,
      this.indexId,
      {
        query: {
          vector: embedding.data[0].embedding,
          topK,
        },
      }
    );

    return results.result.matches;
  }
}

TypeScript Types

All examples are fully typed:

import type {
  ChatCompletionRequest,
  ChatCompletionResponse,
  EmbeddingResponse,
  QueryVectorsResponse,
} from '@cognipeer/cgate-sdk';

Need Help?

• 📖 API Reference
• 🐛 Report Issues