litellm/cookbook/codellama-server

CodeLlama Server: Streaming, Caching, Model Fallbacks (OpenAI + Anthropic), Prompt-tracking

Works with: Anthropic, Huggingface, Cohere, TogetherAI, Azure, OpenAI, etc.

LIVE DEMO - https://litellm.ai/playground

What does CodeLlama Server do

• Uses Together AI's CodeLlama to answer coding questions, with GPT-4 + Claude-2 as backups (you can easily switch this to any model from Huggingface, Replicate, Cohere, AI21, Azure, OpenAI, etc.)
• Sets default system prompt for guardrails system_prompt = "Only respond to questions about code. Say 'I don't know' to anything outside of that."
• Integrates with Promptlayer for model + prompt tracking
• Example output

• Consistent Input/Output Format

  • Call all models using the OpenAI format - completion(model, messages)
  • Text responses will always be available at ['choices'][0]['message']['content']
  • Stream responses will always be available at ['choices'][0]['delta']['content']

• Error Handling Using Model Fallbacks (if CodeLlama fails, try GPT-4) with cooldowns, and retries

• Prompt Logging - Log successful completions to promptlayer for testing + iterating on your prompts in production! (Learn more: https://litellm.readthedocs.io/en/latest/advanced/

  Example: Logs sent to PromptLayer

  

• Token Usage & Spend - Track Input + Completion tokens used + Spend/model - https://docs.litellm.ai/docs/token_usage

• Caching - Provides in-memory cache + GPT-Cache integration for more advanced usage - https://docs.litellm.ai/docs/caching/gpt_cache

• Streaming & Async Support - Return generators to stream text responses - TEST IT 👉 https://litellm.ai/

API Endpoints

/chat/completions (POST)

This endpoint is used to generate chat completions for 50+ support LLM API Models. Use llama2, GPT-4, Claude2 etc

Input

This API endpoint accepts all inputs in raw JSON and expects the following inputs

• prompt (string, required): The user's coding related question
• Additional Optional parameters: temperature, functions, function_call, top_p, n, stream. See the full list of supported inputs here: https://litellm.readthedocs.io/en/latest/input/

Example JSON body

For claude-2

{
  "prompt": "write me a function to print hello world"
}

Making an API request to the Code-Gen Server

import requests
import json

url = "localhost:4000/chat/completions"

payload = json.dumps({
  "prompt": "write me a function to print hello world"
})
headers = {
  'Content-Type': 'application/json'
}

response = requests.request("POST", url, headers=headers, data=payload)

print(response.text)

Output [Response Format]

Responses from the server are given in the following format. All responses from the server are returned in the following format (for all LLM models). More info on output here: https://litellm.readthedocs.io/en/latest/output/

{
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "message": {
                "content": ".\n\n```\ndef print_hello_world():\n    print(\"hello world\")\n",
                "role": "assistant"
            }
        }
    ],
    "created": 1693279694.6474009,
    "model": "togethercomputer/CodeLlama-34b-Instruct",
    "usage": {
        "completion_tokens": 14,
        "prompt_tokens": 28,
        "total_tokens": 42
    }
}

Installation & Usage

Running Locally

1. Clone liteLLM repository to your local machine:

   git clone https://github.com/BerriAI/litellm-CodeLlama-server
   

2. Install the required dependencies using pip

   pip install requirements.txt
   

3. Set your LLM API keys

   os.environ['OPENAI_API_KEY]` = "YOUR_API_KEY"
   or
   set OPENAI_API_KEY in your .env file
   

4. Run the server:

   python main.py
   

Deploying

1. Quick Start: Deploy on Railway

   

2. GCP, AWS, Azure This project includes a Dockerfile allowing you to build and deploy a Docker Project on your providers

Support / Talk with founders

• Our calendar 👋
• Community Discord 💭
• Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238
• Our emails ✉️ ishaan@berri.ai / krrish@berri.ai

Roadmap

• Implement user-based rate-limiting
• Spending controls per project - expose key creation endpoint
• Need to store a keys db -> mapping created keys to their alias (i.e. project name)
• Easily add new models as backups / as the entry-point (add this to the available model list)