adding berrispend integration

litellm/integrations/aispend.py

@@ -0,0 +1,94 @@
+#### What this does ####
+#    On success + failure, log events to aispend.io 
+import dotenv, os
+import requests
+dotenv.load_dotenv() # Loading env variables using dotenv
+import traceback
+import datetime
+
+model_cost = {
+    "gpt-3.5-turbo": {"max_tokens": 4000, "input_cost_per_token": 0.0000015, "output_cost_per_token": 0.000002},
+    "gpt-35-turbo": {"max_tokens": 4000, "input_cost_per_token": 0.0000015, "output_cost_per_token": 0.000002}, # azure model name
+    "gpt-3.5-turbo-0613": {"max_tokens": 4000, "input_cost_per_token": 0.0000015, "output_cost_per_token": 0.000002},
+    "gpt-3.5-turbo-0301": {"max_tokens": 4000, "input_cost_per_token": 0.0000015, "output_cost_per_token": 0.000002},
+    "gpt-3.5-turbo-16k": {"max_tokens": 16000, "input_cost_per_token": 0.000003, "output_cost_per_token": 0.000004},
+    "gpt-35-turbo-16k": {"max_tokens": 16000, "input_cost_per_token": 0.000003, "output_cost_per_token": 0.000004}, # azure model name
+    "gpt-3.5-turbo-16k-0613": {"max_tokens": 16000, "input_cost_per_token": 0.000003, "output_cost_per_token": 0.000004},
+    "gpt-4": {"max_tokens": 8000, "input_cost_per_token": 0.000003, "output_cost_per_token": 0.00006},
+    "gpt-4-0613": {"max_tokens": 8000, "input_cost_per_token": 0.000003, "output_cost_per_token": 0.00006},
+    "gpt-4-32k": {"max_tokens": 8000, "input_cost_per_token": 0.00006, "output_cost_per_token": 0.00012},
+    "claude-instant-1": {"max_tokens": 100000, "input_cost_per_token": 0.00000163, "output_cost_per_token": 0.00000551},
+    "claude-2": {"max_tokens": 100000, "input_cost_per_token": 0.00001102, "output_cost_per_token": 0.00003268},
+    "text-bison-001": {"max_tokens": 8192, "input_cost_per_token": 0.000004, "output_cost_per_token": 0.000004},
+    "chat-bison-001": {"max_tokens": 4096, "input_cost_per_token": 0.000002, "output_cost_per_token": 0.000002},
+    "command-nightly": {"max_tokens": 4096, "input_cost_per_token": 0.000015, "output_cost_per_token": 0.000015},
+}
+
+class AISpendLogger:
+    # Class variables or attributes
+    def __init__(self):
+        # Instance variables
+        self.account_id = os.getenv("AISPEND_ACCOUNT_ID")
+        self.api_key = os.getenv("AISPEND_API_KEY")
+    
+    def price_calculator(self, model, response_obj, start_time, end_time):
+        # try and find if the model is in the model_cost map
+        # else default to the average of the costs
+        prompt_tokens_cost_usd_dollar = 0
+        completion_tokens_cost_usd_dollar = 0
+        if model in model_cost:
+            prompt_tokens_cost_usd_dollar = model_cost[model]["input_cost_per_token"] * response_obj["usage"]["prompt_tokens"]
+            completion_tokens_cost_usd_dollar = model_cost[model]["output_cost_per_token"] * response_obj["usage"]["completion_tokens"]
+        elif "replicate" in model: 
+            # replicate models are charged based on time
+            # llama 2 runs on an nvidia a100 which costs $0.0032 per second - https://replicate.com/replicate/llama-2-70b-chat
+            model_run_time = end_time - start_time # assuming time in seconds
+            cost_usd_dollar = model_run_time * 0.0032
+            prompt_tokens_cost_usd_dollar = cost_usd_dollar / 2
+            completion_tokens_cost_usd_dollar = cost_usd_dollar / 2
+        else:
+            # calculate average input cost 
+            input_cost_sum = 0
+            output_cost_sum = 0
+            for model in model_cost:
+                input_cost_sum += model_cost[model]["input_cost_per_token"]
+                output_cost_sum += model_cost[model]["output_cost_per_token"]
+            avg_input_cost = input_cost_sum / len(model_cost.keys())
+            avg_output_cost = output_cost_sum / len(model_cost.keys())
+            prompt_tokens_cost_usd_dollar = model_cost[model]["input_cost_per_token"] * response_obj["usage"]["prompt_tokens"]
+            completion_tokens_cost_usd_dollar = model_cost[model]["output_cost_per_token"] * response_obj["usage"]["completion_tokens"]
+        return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
+        
+    def log_event(self, model, response_obj, start_time, end_time, print_verbose):
+        # Method definition
+        try:
+            print_verbose(f"AISpend Logging - Enters logging function for model {model}")
+
+            url = f"https://aispend.io/api/v1/accounts/{self.account_id}/data"
+            headers = {
+                'Authorization': f'Bearer {self.api_key}',
+                'Content-Type': 'application/json'
+            }
+
+            response_timestamp = datetime.datetime.fromtimestamp(int(response_obj["created"])).strftime('%Y-%m-%d')
+
+            prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar = self.price_calculator(model, response_obj, start_time, end_time)
+            prompt_tokens_cost_usd_cent = prompt_tokens_cost_usd_dollar * 100
+            completion_tokens_cost_usd_cent = completion_tokens_cost_usd_dollar * 100
+            data = [{
+                "requests": 1,
+                "requests_context": 1,
+                "context_tokens": response_obj["usage"]["prompt_tokens"],
+                "requests_generated": 1,
+                "generated_tokens": response_obj["usage"]["completion_tokens"],
+                "recorded_date": response_timestamp,
+                "model_id": response_obj["model"],
+                "generated_tokens_cost_usd_cent": prompt_tokens_cost_usd_cent,
+                "context_tokens_cost_usd_cent": completion_tokens_cost_usd_cent
+            }]
+
+            print_verbose(f"AISpend Logging - final data object: {data}")
+        except:
+            # traceback.print_exc()
+            print_verbose(f"AISpend Logging Error - {traceback.format_exc()}")
+            pass