Fix ClearML callback (#11472)

Handle different field names in dicts/dataframes, fixing the ClearML
callback.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/langchain/langchain/callbacks/clearml_callback.py

@@ -1,7 +1,9 @@
+from __future__ import annotations
+
 import tempfile
 from copy import deepcopy
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Sequence
+from typing import TYPE_CHECKING, Any, Dict, List, Mapping, Optional, Sequence
 
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.callbacks.utils import (
@@ -15,6 +17,9 @@ from langchain.callbacks.utils import (
 )
 from langchain.schema import AgentAction, AgentFinish, LLMResult
 
+if TYPE_CHECKING:
+    import pandas as pd
+
 
 def import_clearml() -> Any:
     """Import the clearml python package and raise an error if it is not installed."""
@@ -173,7 +178,7 @@ class ClearMLCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         resp.update(flatten_dict(serialized))
         resp.update(self.get_custom_callback_meta())
 
-        chain_input = inputs["input"]
+        chain_input = inputs.get("input", inputs.get("human_input"))
 
         if isinstance(chain_input, str):
             input_resp = deepcopy(resp)
@@ -200,7 +205,12 @@ class ClearMLCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         self.ends += 1
 
         resp = self._init_resp()
-        resp.update({"action": "on_chain_end", "outputs": outputs["output"]})
+        resp.update(
+            {
+                "action": "on_chain_end",
+                "outputs": outputs.get("output", outputs.get("text")),
+            }
+        )
         resp.update(self.get_custom_callback_meta())
 
         self.on_chain_end_records.append(resp)
@@ -372,16 +382,31 @@ class ClearMLCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
 
         return resp
 
+    @staticmethod
+    def _build_llm_df(
+        base_df: pd.DataFrame, base_df_fields: Sequence, rename_map: Mapping
+    ) -> pd.DataFrame:
+        base_df_fields = [field for field in base_df_fields if field in base_df]
+        rename_map = {
+            map_entry_k: map_entry_v
+            for map_entry_k, map_entry_v in rename_map.items()
+            if map_entry_k in base_df_fields
+        }
+        llm_df = base_df[base_df_fields].dropna(axis=1)
+        if rename_map:
+            llm_df = llm_df.rename(rename_map, axis=1)
+        return llm_df
+
     def _create_session_analysis_df(self) -> Any:
         """Create a dataframe with all the information from the session."""
         pd = import_pandas()
-        on_llm_start_records_df = pd.DataFrame(self.on_llm_start_records)
         on_llm_end_records_df = pd.DataFrame(self.on_llm_end_records)
 
-        llm_input_prompts_df = (
+        llm_input_prompts_df = ClearMLCallbackHandler._build_llm_df(
-            on_llm_start_records_df[["step", "prompts", "name"]]
+            base_df=on_llm_end_records_df,
-            .dropna(axis=1)
+            base_df_fields=["step", "prompts"]
-            .rename({"step": "prompt_step"}, axis=1)
+            + (["name"] if "name" in on_llm_end_records_df else ["id"]),
+            rename_map={"step": "prompt_step"},
         )
         complexity_metrics_columns = []
         visualizations_columns: List = []
@@ -406,30 +431,20 @@ class ClearMLCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
                 "osman",
             ]
 
-        llm_outputs_df = (
+        llm_outputs_df = ClearMLCallbackHandler._build_llm_df(
-            on_llm_end_records_df[
+            on_llm_end_records_df,
-                [
+            [
-                    "step",
+                "step",
-                    "text",
+                "text",
-                    "token_usage_total_tokens",
+                "token_usage_total_tokens",
-                    "token_usage_prompt_tokens",
+                "token_usage_prompt_tokens",
-                    "token_usage_completion_tokens",
+                "token_usage_completion_tokens",
-                ]
-                + complexity_metrics_columns
-                + visualizations_columns
             ]
-            .dropna(axis=1)
+            + complexity_metrics_columns
-            .rename({"step": "output_step", "text": "output"}, axis=1)
+            + visualizations_columns,
+            {"step": "output_step", "text": "output"},
         )
         session_analysis_df = pd.concat([llm_input_prompts_df, llm_outputs_df], axis=1)
-        # session_analysis_df["chat_html"] = session_analysis_df[
-        #     ["prompts", "output"]
-        # ].apply(
-        #     lambda row: construct_html_from_prompt_and_generation(
-        #         row["prompts"], row["output"]
-        #     ),
-        #     axis=1,
-        # )
         return session_analysis_df
 
     def flush_tracker(