update cookbook

cookbook/codellama-server/README.MD

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+# CodeLlama Server: Streaming, Caching, Model Fallbacks (OpenAI + Anthropic), Prompt-tracking
+
+Works with: Anthropic, Huggingface, Cohere, TogetherAI, Azure, OpenAI, etc.
+
+[![PyPI Version](https://img.shields.io/pypi/v/litellm.svg)](https://pypi.org/project/litellm/)
+[![PyPI Version](https://img.shields.io/badge/stable%20version-v0.1.345-blue?color=green&link=https://pypi.org/project/litellm/0.1.1/)](https://pypi.org/project/litellm/0.1.1/)
+![Downloads](https://img.shields.io/pypi/dm/litellm)
+
+[![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/HuDPw-?referralCode=jch2ME)
+
+**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
+
+<img src="imgs/code-output.png" alt="Code Output" width="600"/>
+
+- **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**
+
+    <img src="imgs/promptlayer_logging.png" alt="Prompt Logging" width="900"/>
+
+
+- **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
+
+```json
+{
+  "prompt": "write me a function to print hello world"
+}
+```
+
+### Making an API request to the Code-Gen Server
+
+```python
+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/
+
+```json
+{
+    "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-CodeGen-proxy
+   ```
+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
+
+   [![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/HuDPw-?referralCode=jch2ME)
+
+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 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
+- [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
+- 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)

cookbook/codellama-server/main.py

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+import traceback
+from flask import Flask, request, jsonify, abort, Response
+from flask_cors import CORS
+import traceback
+import litellm
+from util import handle_error
+from litellm import completion 
+import os, dotenv, time 
+import json
+dotenv.load_dotenv()
+
+# TODO: set your keys in .env or here:
+# os.environ["OPENAI_API_KEY"] = "" # set your openai key here
+# os.environ["ANTHROPIC_API_KEY"] = "" # set your anthropic key here
+# os.environ["TOGETHER_AI_API_KEY"] = "" # set your together ai key here
+# see supported models / keys here: https://litellm.readthedocs.io/en/latest/supported/
+######### ENVIRONMENT VARIABLES ##########
+verbose = True
+
+# litellm.caching_with_models = True # CACHING: caching_with_models Keys in the cache are messages + model. - to learn more: https://docs.litellm.ai/docs/caching/
+######### PROMPT LOGGING ##########
+os.environ["PROMPTLAYER_API_KEY"] = "" # set your promptlayer key here - https://promptlayer.com/
+
+# set callbacks
+litellm.success_callback = ["promptlayer"]
+############ HELPER FUNCTIONS ###################################
+
+def print_verbose(print_statement):
+    if verbose:
+        print(print_statement)
+
+app = Flask(__name__)
+CORS(app)
+
+@app.route('/')
+def index():
+    return 'received!', 200
+
+def data_generator(response):
+    for chunk in response:
+        yield f"data: {json.dumps(chunk)}\n\n"
+
+@app.route('/chat/completions', methods=["POST"])
+def api_completion():
+    data = request.json
+    start_time = time.time() 
+    if data.get('stream') == "True":
+        data['stream'] = True # convert to boolean
+    try:
+        if "prompt" not in data:
+            raise ValueError("data needs to have prompt")
+        data["model"] = "togethercomputer/CodeLlama-34b-Instruct" # by default use Together AI's CodeLlama model - https://api.together.xyz/playground/chat?model=togethercomputer%2FCodeLlama-34b-Instruct
+        # COMPLETION CALL
+        system_prompt = "Only respond to questions about code. Say 'I don't know' to anything outside of that."
+        messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": data.pop("prompt")}]
+        data["messages"] = messages
+        print(f"data: {data}")
+        response = completion(**data)
+        ## LOG SUCCESS
+        end_time = time.time() 
+        if 'stream' in data and data['stream'] == True: # use generate_responses to stream responses
+            return Response(data_generator(response), mimetype='text/event-stream')
+    except Exception as e:
+        # call handle_error function
+        print_verbose(f"Got Error api_completion(): {traceback.format_exc()}")
+        ## LOG FAILURE
+        end_time = time.time() 
+        traceback_exception = traceback.format_exc()
+        return handle_error(data=data)
+    return response
+
+@app.route('/get_models', methods=["POST"])
+def get_models():
+    try:
+        return litellm.model_list
+    except Exception as e:
+        traceback.print_exc()
+        response = {"error": str(e)}
+    return response, 200
+
+if __name__ == "__main__":
+  from waitress import serve
+  serve(app, host="0.0.0.0", port=4000, threads=500)
+