feat: add logfire integration

litellm/integrations/logfire_logger.py

@@ -0,0 +1,145 @@
+#### What this does ####
+#    On success + failure, log events to Logfire
+
+import dotenv, os
+
+dotenv.load_dotenv()  # Loading env variables using dotenv
+import traceback
+import uuid
+from litellm._logging import print_verbose, verbose_logger
+
+from typing import Any, Dict, NamedTuple
+from typing_extensions import LiteralString
+
+
+class SpanConfig(NamedTuple):
+    message_template: LiteralString
+    span_data: Dict[str, Any]
+
+
+class LogfireLogger:
+    # Class variables or attributes
+    def __init__(self):
+        try:
+            verbose_logger.debug(f"in init logfire logger")
+            import logfire
+
+            # only setting up logfire if we are sending to logfire
+            # in testing, we don't want to send to logfire
+            if logfire.DEFAULT_LOGFIRE_INSTANCE.config.send_to_logfire:
+                logfire.configure(token=os.getenv("LOGFIRE_TOKEN"))
+        except Exception as e:
+            print_verbose(f"Got exception on init logfire client {str(e)}")
+            raise e
+
+    def _get_span_config(self, payload) -> SpanConfig:
+        if (
+            payload["call_type"] == "completion"
+            or payload["call_type"] == "acompletion"
+        ):
+            return SpanConfig(
+                message_template="Chat Completion with {request_data[model]!r}",
+                span_data={"request_data": payload},
+            )
+        elif (
+            payload["call_type"] == "embedding" or payload["call_type"] == "aembedding"
+        ):
+            return SpanConfig(
+                message_template="Embedding Creation with {request_data[model]!r}",
+                span_data={"request_data": payload},
+            )
+        elif (
+            payload["call_type"] == "image_generation"
+            or payload["call_type"] == "aimage_generation"
+        ):
+            return SpanConfig(
+                message_template="Image Generation with {request_data[model]!r}",
+                span_data={"request_data": payload},
+            )
+        else:
+            return SpanConfig(
+                message_template="Litellm Call with {request_data[model]!r}",
+                span_data={"request_data": payload},
+            )
+
+    async def _async_log_event(
+        self, kwargs, response_obj, start_time, end_time, print_verbose, user_id
+    ):
+        self.log_event(kwargs, response_obj, start_time, end_time, print_verbose)
+
+    def log_event(
+        self, kwargs, response_obj, start_time, end_time, user_id, print_verbose
+    ):
+        try:
+            import logfire
+
+            verbose_logger.debug(
+                f"logfire Logging - Enters logging function for model {kwargs}"
+            )
+            litellm_params = kwargs.get("litellm_params", {})
+            metadata = (
+                litellm_params.get("metadata", {}) or {}
+            )  # if litellm_params['metadata'] == None
+            messages = kwargs.get("messages")
+            optional_params = kwargs.get("optional_params", {})
+            call_type = kwargs.get("call_type", "completion")
+            cache_hit = kwargs.get("cache_hit", False)
+            usage = response_obj.get("usage", {})
+            id = response_obj.get("id", str(uuid.uuid4()))
+            try:
+                response_time = (end_time - start_time).total_seconds()
+            except:
+                response_time = None
+
+            # Clean Metadata before logging - never log raw metadata
+            # the raw metadata can contain circular references which leads to infinite recursion
+            # we clean out all extra litellm metadata params before logging
+            clean_metadata = {}
+            if isinstance(metadata, dict):
+                for key, value in metadata.items():
+                    # clean litellm metadata before logging
+                    if key in [
+                        "endpoint",
+                        "caching_groups",
+                        "previous_models",
+                    ]:
+                        continue
+                    else:
+                        clean_metadata[key] = value
+
+            # Build the initial payload
+            payload = {
+                "id": id,
+                "call_type": call_type,
+                "cache_hit": cache_hit,
+                "startTime": start_time,
+                "endTime": end_time,
+                "responseTime (seconds)": response_time,
+                "model": kwargs.get("model", ""),
+                "user": kwargs.get("user", ""),
+                "modelParameters": optional_params,
+                "spend": kwargs.get("response_cost", 0),
+                "messages": messages,
+                "response": response_obj,
+                "usage": usage,
+                "metadata": clean_metadata,
+            }
+            logfire_openai = logfire.with_settings(custom_scope_suffix="openai")
+
+            message_template, span_data = self._get_span_config(payload)
+            with logfire_openai.span(
+                message_template,
+                **span_data,
+            ):
+                pass
+            print_verbose(f"\ndd Logger - Logging payload = {payload}")
+
+            print_verbose(
+                f"Logfire Layer Logging - final response object: {response_obj}"
+            )
+        except Exception as e:
+            traceback.print_exc()
+            verbose_logger.debug(
+                f"Logfire Layer Error - {str(e)}\n{traceback.format_exc()}"
+            )
+            pass