None Type LlamaIndex payload

portkey_ai/langchain/__init__.py

@@ -0,0 +1,3 @@
+from .portkey_langchain_callback_handler import LangchainCallbackHandler
+
+__all__ = ["LangchainCallbackHandler"]

portkey_ai/llms/langchain/portkey_langchain_callback.py → portkey_ai/langchain/portkey_langchain_callback_handler.py

@@ -13,7 +13,7 @@
     raise ImportError("Please pip install langchain-core to use PortkeyLangchain")
 
 
-class PortkeyLangchain(BaseCallbackHandler):
+class LangchainCallbackHandler(BaseCallbackHandler):
     def __init__(
         self,
         api_key: str,

portkey_ai/llamaindex/__init__.py

@@ -0,0 +1,3 @@
+from .portkey_llama_callback_handler import LlamaIndexCallbackHandler
+
+__all__ = ["LlamaIndexCallbackHandler"]

portkey_ai/llms/llama_index/portkey_llama_callback.py → portkey_ai/llamaindex/portkey_llama_callback_handler.py

@@ -23,7 +23,7 @@
     raise ImportError("Please pip install llama-index to use Portkey Callback Handler")
 
 
-class PortkeyLlamaindex(LlamaIndexBaseCallbackHandler):
+class LlamaIndexCallbackHandler(LlamaIndexBaseCallbackHandler):
     def __init__(
         self,
         api_key: str,
@@ -126,30 +126,38 @@ def on_event_end(
         """Run when an event ends."""
         span_id = event_id
 
-        if event_type == "llm":
-            response_payload = self.llm_event_end(payload, event_id)
-        elif event_type == "embedding":
-            response_payload = self.embedding_event_end(payload, event_id)
-        elif event_type == "agent_step":
-            response_payload = self.agent_step_event_end(payload, event_id)
-        elif event_type == "function_call":
-            response_payload = self.function_call_event_end(payload, event_id)
-        elif event_type == "query":
-            response_payload = self.query_event_end(payload, event_id)
-        elif event_type == "retrieve":
-            response_payload = self.retrieve_event_end(payload, event_id)
-        elif event_type == "templating":
-            response_payload = self.templating_event_end(payload, event_id)
+        if payload is None:
+            response_payload = {}
+            if span_id in self.event_map:
+                event = self.event_map[event_id]
+                start_time = event["start_time"]
+                end_time = int(datetime.now().timestamp())
+                total_time = (end_time - start_time) * 1000
+                response_payload["response_time"] = total_time
         else:
-            response_payload = payload
+            if event_type == "llm":
+                response_payload = self.llm_event_end(payload, event_id)
+            elif event_type == "embedding":
+                response_payload = self.embedding_event_end(payload, event_id)
+            elif event_type == "agent_step":
+                response_payload = self.agent_step_event_end(payload, event_id)
+            elif event_type == "function_call":
+                response_payload = self.function_call_event_end(payload, event_id)
+            elif event_type == "query":
+                response_payload = self.query_event_end(payload, event_id)
+            elif event_type == "retrieve":
+                response_payload = self.retrieve_event_end(payload, event_id)
+            elif event_type == "templating":
+                response_payload = self.templating_event_end(payload, event_id)
+            else:
+                response_payload = payload
 
         self.event_map[span_id]["response"] = response_payload
 
         self.event_array.append(self.event_map[span_id])
 
     def start_trace(self, trace_id: Optional[str] = None) -> None:
         """Run when an overall trace is launched."""
-
         if trace_id == "index_construction":
             self.global_trace_id = self.metadata.get("traceId", str(uuid4()))  # type: ignore [union-attr]
 
@@ -230,7 +238,7 @@ def llm_event_end(self, payload: Any, event_id) -> Any:
         )
         self.response["body"].update({"id": event_id})
         self.response["body"].update({"created": int(time.time())})
-        self.response["body"].update({"model": data.raw.get("model", "")})
+        self.response["body"].update({"model": getattr(data, "model", "")})
         self.response["headers"] = {}
         self.response["streamingMode"] = self.streamingMode
 

portkey_ai/llms/langchain/__init__.py

@@ -1,5 +1,7 @@
 from .chat import ChatPortkey
 from .completion import PortkeyLLM
-from .portkey_langchain_callback import PortkeyLangchain
 
-__all__ = ["ChatPortkey", "PortkeyLLM", "PortkeyLangchain"]
+__all__ = [
+    "ChatPortkey",
+    "PortkeyLLM",
+]

portkey_ai/llms/llama_index/__init__.py

@@ -1,3 +0,0 @@
-from .portkey_llama_callback import PortkeyLlamaindex
-
-__all__ = ["PortkeyLlamaindex"]

tests/test_llm_langchain.py

@@ -6,7 +6,7 @@
 import pytest
 
 from tests.utils import read_json_file
-from portkey_ai.llms.langchain import PortkeyLangchain
+from portkey_ai.langchain import LangchainCallbackHandler
 from langchain.chat_models import ChatOpenAI
 from langchain_core.prompts import ChatPromptTemplate
 from langchain.chains import LLMChain
@@ -15,7 +15,7 @@
 
 
 class TestLLMLangchain:
-    client = PortkeyLangchain
+    client = LangchainCallbackHandler
     parametrize = pytest.mark.parametrize("client", [client], ids=["strict"])
     models = read_json_file("./tests/models.json")
 

tests/test_llm_llamaindex.py

@@ -6,7 +6,7 @@
 import pytest
 
 from tests.utils import read_json_file
-from portkey_ai.llms.llama_index import PortkeyLlamaindex
+from portkey_ai.llamaindex import LlamaIndexCallbackHandler
 
 
 from llama_index.llms.openai import OpenAI
@@ -24,7 +24,7 @@
 
 
 class TestLLMLlamaindex:
-    client = PortkeyLlamaindex
+    client = LlamaIndexCallbackHandler
     parametrize = pytest.mark.parametrize("client", [client], ids=["strict"])
     models = read_json_file("./tests/models.json")