structure saas with tools

.venv/lib/python3.10/site-packages/litellm/integrations/prompt_layer.py

@@ -0,0 +1,91 @@
+#### What this does ####
+#    On success, logs events to Promptlayer
+import os
+import traceback
+
+from pydantic import BaseModel
+
+import litellm
+
+
+class PromptLayerLogger:
+    # Class variables or attributes
+    def __init__(self):
+        # Instance variables
+        self.key = os.getenv("PROMPTLAYER_API_KEY")
+
+    def log_event(self, kwargs, response_obj, start_time, end_time, print_verbose):
+        # Method definition
+        try:
+            new_kwargs = {}
+            new_kwargs["model"] = kwargs["model"]
+            new_kwargs["messages"] = kwargs["messages"]
+
+            # add kwargs["optional_params"] to new_kwargs
+            for optional_param in kwargs["optional_params"]:
+                new_kwargs[optional_param] = kwargs["optional_params"][optional_param]
+
+            # Extract PromptLayer tags from metadata, if such exists
+            tags = []
+            metadata = {}
+            if "metadata" in kwargs["litellm_params"]:
+                if "pl_tags" in kwargs["litellm_params"]["metadata"]:
+                    tags = kwargs["litellm_params"]["metadata"]["pl_tags"]
+
+                # Remove "pl_tags" from metadata
+                metadata = {
+                    k: v
+                    for k, v in kwargs["litellm_params"]["metadata"].items()
+                    if k != "pl_tags"
+                }
+
+            print_verbose(
+                f"Prompt Layer Logging - Enters logging function for model kwargs: {new_kwargs}\n, response: {response_obj}"
+            )
+
+            # python-openai >= 1.0.0 returns Pydantic objects instead of jsons
+            if isinstance(response_obj, BaseModel):
+                response_obj = response_obj.model_dump()
+
+            request_response = litellm.module_level_client.post(
+                "https://api.promptlayer.com/rest/track-request",
+                json={
+                    "function_name": "openai.ChatCompletion.create",
+                    "kwargs": new_kwargs,
+                    "tags": tags,
+                    "request_response": dict(response_obj),
+                    "request_start_time": int(start_time.timestamp()),
+                    "request_end_time": int(end_time.timestamp()),
+                    "api_key": self.key,
+                    # Optional params for PromptLayer
+                    # "prompt_id": "<PROMPT ID>",
+                    # "prompt_input_variables": "<Dictionary of variables for prompt>",
+                    # "prompt_version":1,
+                },
+            )
+
+            response_json = request_response.json()
+            if not request_response.json().get("success", False):
+                raise Exception("Promptlayer did not successfully log the response!")
+
+            print_verbose(
+                f"Prompt Layer Logging: success - final response object: {request_response.text}"
+            )
+
+            if "request_id" in response_json:
+                if metadata:
+                    response = litellm.module_level_client.post(
+                        "https://api.promptlayer.com/rest/track-metadata",
+                        json={
+                            "request_id": response_json["request_id"],
+                            "api_key": self.key,
+                            "metadata": metadata,
+                        },
+                    )
+                    print_verbose(
+                        f"Prompt Layer Logging: success - metadata post response object: {response.text}"
+                    )
+
+        except Exception:
+            print_verbose(f"error: Prompt Layer Error - {traceback.format_exc()}")
+            pass