Added LiteLLM to the stack

Development/litellm/cookbook/benchmark/benchmark.py

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+from litellm import completion, completion_cost
+import time
+import click
+from tqdm import tqdm
+from tabulate import tabulate
+from termcolor import colored
+import os
+
+
+# Define the list of models to benchmark
+# select any LLM listed here: https://docs.litellm.ai/docs/providers
+models = ["gpt-3.5-turbo", "claude-2"]
+
+# Enter LLM API keys
+# https://docs.litellm.ai/docs/providers
+os.environ["OPENAI_API_KEY"] = ""
+os.environ["ANTHROPIC_API_KEY"] = ""
+
+# List of questions to benchmark (replace with your questions)
+questions = ["When will BerriAI IPO?", "When will LiteLLM hit $100M ARR?"]
+
+# Enter your system prompt here
+system_prompt = """
+You are LiteLLMs helpful assistant
+"""
+
+
+@click.command()
+@click.option(
+    "--system-prompt",
+    default="You are a helpful assistant that can answer questions.",
+    help="System prompt for the conversation.",
+)
+def main(system_prompt):
+    for question in questions:
+        data = []  # Data for the current question
+
+        with tqdm(total=len(models)) as pbar:
+            for model in models:
+                colored_description = colored(
+                    f"Running question: {question} for model: {model}", "green"
+                )
+                pbar.set_description(colored_description)
+                start_time = time.time()
+
+                response = completion(
+                    model=model,
+                    max_tokens=500,
+                    messages=[
+                        {"role": "system", "content": system_prompt},
+                        {"role": "user", "content": question},
+                    ],
+                )
+
+                end = time.time()
+                total_time = end - start_time
+                cost = completion_cost(completion_response=response)
+                raw_response = response["choices"][0]["message"]["content"]
+
+                data.append(
+                    {
+                        "Model": colored(model, "light_blue"),
+                        "Response": raw_response,  # Colorize the response
+                        "ResponseTime": colored(f"{total_time:.2f} seconds", "red"),
+                        "Cost": colored(f"${cost:.6f}", "green"),  # Colorize the cost
+                    }
+                )
+
+                pbar.update(1)
+
+        # Separate headers from the data
+        headers = ["Model", "Response", "Response Time (seconds)", "Cost ($)"]
+        colwidths = [15, 80, 15, 10]
+
+        # Create a nicely formatted table for the current question
+        table = tabulate(
+            [list(d.values()) for d in data],
+            headers,
+            tablefmt="grid",
+            maxcolwidths=colwidths,
+        )
+
+        # Print the table for the current question
+        colored_question = colored(question, "green")
+        click.echo(f"\nBenchmark Results for '{colored_question}':")
+        click.echo(table)  # Display the formatted table
+
+
+if __name__ == "__main__":
+    main()