https://github.com/faizancodes/llm-chain
A TypeScript library for easily interacting with various Large Language Model providers through a unified interface.
https://github.com/faizancodes/llm-chain
anthropic gemini groq openai typescript
Last synced: 5 months ago
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A TypeScript library for easily interacting with various Large Language Model providers through a unified interface.
- Host: GitHub
- URL: https://github.com/faizancodes/llm-chain
- Owner: faizancodes
- License: mit
- Created: 2024-12-21T06:46:56.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2025-01-08T02:20:36.000Z (9 months ago)
- Last Synced: 2025-04-13T00:07:08.355Z (6 months ago)
- Topics: anthropic, gemini, groq, openai, typescript
- Language: TypeScript
- Homepage: https://www.npmjs.com/package/llm-chain
- Size: 323 KB
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# llm-chain
A TypeScript library for easily interacting with various Large Language Model providers through a unified interface. Equipped with performance metrics for response time and streaming, useful for benchmarking and testing.
## Installation
```bash
npm install llm-chain
```
## Features
- Unified interface for multiple LLM providers:
- OpenAI
- Groq
- Gemini (Google)
- Anthropic
- Together AI
- DeepSeek
- XAI
- Type-safe API with TypeScript
- Support for streaming responses
- Built-in validation using Zod
- Easy to extend for new providers
- Automatic model validation and token limits
- Provider-specific optimizations
- Support for multiple deployment options (Direct API, AWS Bedrock, Google Vertex)
## Usage
### OpenAI
```typescript
import { LLMClient } from "llm-chain";
// Create a client with OpenAI
const client = LLMClient.createOpenAI("your-openai-api-key");
// Simple completion
const response = await client.complete("What is the capital of France?");
console.log(response);
// Chat completion with more options
const chatResponse = await client.chatCompletion({
model: "gpt-4o-mini",
messages: [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "What is the capital of France?" },
],
temperature: 0.7,
});
console.log(chatResponse.message.content);
```
### Groq
```typescript
import { LLMClient } from "llm-chain";
// Create a client with Groq
const client = LLMClient.createGroq("your-groq-api-key");
// Chat completion with Mixtral
const chatResponse = await client.chatCompletion({
model: "mixtral-8x7b-32768",
messages: [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "What is the capital of France?" },
],
temperature: 0.7,
});
console.log(chatResponse.message.content);
```
### Gemini
```typescript
import { LLMClient } from "llm-chain";
// Create a client with Gemini
const client = LLMClient.createGemini("your-gemini-api-key");
// Chat completion with Gemini Pro
const chatResponse = await client.chatCompletion({
model: "gemini-2.0-flash",
messages: [{ role: "user", content: "What is the capital of France?" }],
temperature: 0.7,
});
console.log(chatResponse.message.content);
```
### Anthropic
```typescript
import { LLMClient } from "llm-chain";
// Create a client with Anthropic
const client = LLMClient.createAnthropic("your-anthropic-api-key");
// Chat completion with Claude
const chatResponse = await client.chatCompletion({
model: "claude-3.5-sonnet-latest",
messages: [{ role: "user", content: "What is the capital of France?" }],
temperature: 0.7,
});
console.log(chatResponse.message.content);
```
### Streaming Responses
```typescript
await client.streamChatCompletion(
{
model: "mixtral-8x7b-32768", // or any other supported model
messages: [{ role: "user", content: "Write a story about a cat." }],
},
chunk => {
process.stdout.write(chunk);
}
);
```
## Supported Models
### OpenAI Models
- Default model: gpt-4o-mini
- Supports all OpenAI chat models
### Groq Models
#### Production Models
- `mixtral-8x7b-32768` (Mistral, 32k context)
- `llama-3.3-70b-versatile` (Meta, 128k context)
- `llama-3.1-8b-instant` (Meta, 128k context)
- Various Whisper models for audio processing
#### Preview Models
- `llama3-groq-70b-8192-tool-use-preview` (Groq)
- `llama3-groq-8b-8192-tool-use-preview` (Groq)
- Various LLaMA-3 preview models
### Gemini Models
#### Gemini 2.0
- `gemini-2.0-flash-exp` (Experimental, 1M input tokens)
#### Gemini 1.5
- `gemini-1.5-flash` (32k context, versatile)
- `gemini-1.5-flash-8b` (32k context, high volume)
- `gemini-1.5-pro` (32k context, complex reasoning)
### Anthropic Models
- `claude-3-5-sonnet-20241022` (Latest Sonnet)
- `claude-3-5-haiku-20241022` (Latest Haiku)
- `claude-3-opus-20240229` (Most powerful)
- `claude-3-sonnet-20240229` (Balanced)
- `claude-3-haiku-20240307` (Fast)
- `claude-2.1` (Legacy)
#### DeepSeek
- `deepseek-chat` (Latest)
#### XAI
- `grok-2-latest` (Latest)
## Advanced Features
### Custom Provider
```typescript
import { LLMProvider, LLMClient } from "llm-chain";
class CustomProvider implements LLMProvider {
// Implement the LLMProvider interface
// ...
}
const client = new LLMClient(new CustomProvider(), "your-model-name");
```
### Anthropic Deployment Options
```typescript
// Direct API
const client = LLMClient.createAnthropic("your-api-key");
// AWS Bedrock
const client = LLMClient.createAnthropic("your-api-key", "bedrock", {
aws_access_key: "your-access-key",
aws_secret_key: "your-secret-key",
aws_region: "us-east-1",
});
// Google Vertex
const client = LLMClient.createAnthropic("your-api-key", "vertex", {
project_id: "your-project-id",
region: "us-central1",
});
```
## Error Handling
The library provides built-in error handling and validation:
- Model availability checks
- Token limit validation
- Context window size validation
- Streaming capability checks
- Provider-specific error handling
- Performance metrics for response time and streaming
## License
MIT