add integration files

docs/my-website/docs/observability/integrations.md

@@ -1,12 +1,9 @@
 # Logging Integrations
 
 | Integration | Required OS Variables                                    | How to Use with callbacks                |
-|-----------------|--------------------------------------------|-------------------------------------------|
+| ----------- | -------------------------------------------------------- | ---------------------------------------- |
+| LLMonitor   | `LLMONITOR_APP_ID`                                       | `litellm.success_callback=["llmonitor"]` |
 | Sentry      | `SENTRY_API_URL`                                         | `litellm.success_callback=["sentry"]`    |
 | Posthog     | `POSTHOG_API_KEY`,`POSTHOG_API_URL`                      | `litellm.success_callback=["posthog"]`   |
 | Slack       | `SLACK_API_TOKEN`,`SLACK_API_SECRET`,`SLACK_API_CHANNEL` | `litellm.success_callback=["slack"]`     |
 | Helicone    | `HELICONE_API_TOKEN`                                     | `litellm.success_callback=["helicone"]`  |
-
-
-
-

litellm/integrations/llmonitor.py

@@ -0,0 +1,61 @@
+#### What this does ####
+#    On success + failure, log events to aispend.io
+import datetime
+import traceback
+import dotenv
+import os
+import requests
+dotenv.load_dotenv()  # Loading env variables using dotenv
+
+
+class LLMonitorLogger:
+    # Class variables or attributes
+    def __init__(self):
+        # Instance variables
+        self.api_url = os.getenv(
+            "LLMONITOR_API_URL") or "https://app.llmonitor.com"
+        self.account_id = os.getenv("LLMONITOR_APP_ID")
+
+    def log_event(self, model, messages, response_obj, start_time, end_time, print_verbose):
+        # Method definition
+        try:
+            print_verbose(
+                f"LLMonitor Logging - Enters logging function for model {model}")
+
+            print(model, messages, response_obj, start_time, end_time)
+
+            # headers = {
+            #     'Content-Type': 'application/json'
+            # }
+
+            # prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar = self.price_calculator(
+            #     model, response_obj, start_time, end_time)
+            # total_cost = prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar
+
+            # response_time = (end_time-start_time).total_seconds()
+            # if "response" in response_obj:
+            #     data = [{
+            #         "response_time": response_time,
+            #         "model_id": response_obj["model"],
+            #         "total_cost": total_cost,
+            #         "messages": messages,
+            #         "response": response_obj['choices'][0]['message']['content'],
+            #         "account_id": self.account_id
+            #     }]
+            # elif "error" in response_obj:
+            #     data = [{
+            #         "response_time": response_time,
+            #         "model_id": response_obj["model"],
+            #         "total_cost": total_cost,
+            #         "messages": messages,
+            #         "error": response_obj['error'],
+            #         "account_id": self.account_id
+            #     }]
+
+            # print_verbose(f"BerriSpend Logging - final data object: {data}")
+            # response = requests.post(url, headers=headers, json=data)
+        except:
+            # traceback.print_exc()
+            print_verbose(
+                f"LLMonitor Logging Error - {traceback.format_exc()}")
+            pass

litellm/tests/test_llmonitor_integration.py

@@ -0,0 +1,28 @@
+#### What this tests ####
+#    This tests if logging to the helicone integration actually works
+
+from litellm import embedding, completion
+import litellm
+import sys
+import os
+import traceback
+import pytest
+
+# Adds the parent directory to the system path
+sys.path.insert(0, os.path.abspath('../..'))
+
+litellm.success_callback = ["llmonitor"]
+
+litellm.set_verbose = True
+
+user_message = "Hello, how are you?"
+messages = [{"content": user_message, "role": "user"}]
+
+
+# openai call
+response = completion(model="gpt-3.5-turbo",
+                      messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
+
+# cohere call
+# response = completion(model="command-nightly",
+# messages=[{"role": "user", "content": "Hi 👋 - i'm cohere"}])