community[patch] : adds feedback and status for Fiddler callback handler events (#19157)

**Description:** This PR adds updates the fiddler events schema to also
pass user feedback, and llm status to fiddler
   **Tickets:** [INTERNAL] FDL-17559 
   **Dependencies:**  NA
   **Twitter handle:** behalder

Co-authored-by: Barun Halder <barun@fiddler.ai>

libs/community/langchain_community/callbacks/fiddler_callback.py

@@ -1,5 +1,6 @@
 import time
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
+from uuid import UUID
 
 from langchain_core.callbacks import BaseCallbackHandler
 from langchain_core.outputs import LLMResult
@@ -15,6 +16,11 @@ PROMPT_TOKENS = "prompt_tokens"
 COMPLETION_TOKENS = "completion_tokens"
 RUN_ID = "run_id"
 MODEL_NAME = "model_name"
+GOOD = "good"
+BAD = "bad"
+NEUTRAL = "neutral"
+SUCCESS = "success"
+FAILURE = "failure"
 
 # Default values
 DEFAULT_MAX_TOKEN = 65536
@@ -23,12 +29,20 @@ DEFAULT_MAX_DURATION = 120
 # Fiddler specific constants
 PROMPT = "prompt"
 RESPONSE = "response"
+CONTEXT = "context"
 DURATION = "duration"
+FEEDBACK = "feedback"
+LLM_STATUS = "llm_status"
+
+FEEDBACK_POSSIBLE_VALUES = [GOOD, BAD, NEUTRAL]
 
 # Define a dataset dictionary
 _dataset_dict = {
     PROMPT: ["fiddler"] * 10,
     RESPONSE: ["fiddler"] * 10,
+    CONTEXT: ["fiddler"] * 10,
+    FEEDBACK: ["good"] * 10,
+    LLM_STATUS: ["success"] * 10,
     MODEL_NAME: ["fiddler"] * 10,
     RUN_ID: ["123e4567-e89b-12d3-a456-426614174000"] * 10,
     TOTAL_TOKENS: [0, DEFAULT_MAX_TOKEN] * 5,
@@ -83,8 +97,9 @@ class FiddlerCallbackHandler(BaseCallbackHandler):
         self.api_key = api_key
         self._df = self.pd.DataFrame(_dataset_dict)
 
-        self.run_id_prompts: Dict[str, List[str]] = {}
-        self.run_id_starttime: Dict[str, int] = {}
+        self.run_id_prompts: Dict[UUID, List[str]] = {}
+        self.run_id_response: Dict[UUID, List[str]] = {}
+        self.run_id_starttime: Dict[UUID, int] = {}
 
         # Initialize Fiddler client here
         self.fiddler_client = self.fdl.FiddlerApi(url, org_id=org, auth_token=api_key)
@@ -105,6 +120,17 @@ class FiddlerCallbackHandler(BaseCallbackHandler):
         dataset_info = self.fdl.DatasetInfo.from_dataframe(
             self._df, max_inferred_cardinality=0
         )
+
+        # Set feedback column to categorical
+        for i in range(len(dataset_info.columns)):
+            if dataset_info.columns[i].name == FEEDBACK:
+                dataset_info.columns[i].data_type = self.fdl.DataType.CATEGORY
+                dataset_info.columns[i].possible_values = FEEDBACK_POSSIBLE_VALUES
+
+            elif dataset_info.columns[i].name == LLM_STATUS:
+                dataset_info.columns[i].data_type = self.fdl.DataType.CATEGORY
+                dataset_info.columns[i].possible_values = [SUCCESS, FAILURE]
+
         if self.model not in self.fiddler_client.get_dataset_names(self.project):
             print(  # noqa: T201
                 f"adding dataset {self.model} to project {self.project}."
@@ -128,13 +154,15 @@ class FiddlerCallbackHandler(BaseCallbackHandler):
             dataset_info=dataset_info,
             dataset_id="train",
             model_task=self.fdl.ModelTask.LLM,
-            features=[PROMPT, RESPONSE],
+            features=[PROMPT, RESPONSE, CONTEXT],
+            target=FEEDBACK,
             metadata_cols=[
                 RUN_ID,
                 TOTAL_TOKENS,
                 PROMPT_TOKENS,
                 COMPLETION_TOKENS,
                 MODEL_NAME,
+                DURATION,
             ],
             custom_features=self.custom_features,
         )
@@ -228,6 +256,42 @@ class FiddlerCallbackHandler(BaseCallbackHandler):
             ),
         ]
 
+    def _publish_events(
+        self,
+        run_id: UUID,
+        prompt_responses: List[str],
+        duration: int,
+        llm_status: str,
+        model_name: Optional[str] = "",
+        token_usage_dict: Optional[Dict[str, Any]] = None,
+    ) -> None:
+        """
+        Publish events to fiddler
+        """
+
+        prompt_count = len(self.run_id_prompts[run_id])
+        df = self.pd.DataFrame(
+            {
+                PROMPT: self.run_id_prompts[run_id],
+                RESPONSE: prompt_responses,
+                RUN_ID: [str(run_id)] * prompt_count,
+                DURATION: [duration] * prompt_count,
+                LLM_STATUS: [llm_status] * prompt_count,
+                MODEL_NAME: [model_name] * prompt_count,
+            }
+        )
+
+        if token_usage_dict:
+            for key, value in token_usage_dict.items():
+                df[key] = [value] * prompt_count if isinstance(value, int) else value
+
+        try:
+            self.fiddler_client.publish_events_batch(self.project, self.model, df)
+        except Exception as e:
+            print(  # noqa: T201
+                f"Error publishing events to fiddler: {e}. continuing..."
+            )
+
     def on_llm_start(
         self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
     ) -> Any:
@@ -237,42 +301,36 @@ class FiddlerCallbackHandler(BaseCallbackHandler):
 
     def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
         flattened_llmresult = response.flatten()
-        token_usage_dict = {}
         run_id = kwargs[RUN_ID]
         run_duration = self.run_id_starttime[run_id] - int(time.time())
-        prompt_responses = []
         model_name = ""
+        token_usage_dict = {}
 
         if isinstance(response.llm_output, dict):
-            if TOKEN_USAGE in response.llm_output:
-                token_usage_dict = response.llm_output[TOKEN_USAGE]
-            if MODEL_NAME in response.llm_output:
-                model_name = response.llm_output[MODEL_NAME]
-
-        for llmresult in flattened_llmresult:
-            prompt_responses.append(llmresult.generations[0][0].text)
-
-        df = self.pd.DataFrame(
-            {
-                PROMPT: self.run_id_prompts[run_id],
-                RESPONSE: prompt_responses,
+            token_usage_dict = {
+                k: v
+                for k, v in response.llm_output.items()
+                if k in [TOTAL_TOKENS, PROMPT_TOKENS, COMPLETION_TOKENS]
             }
-        )
-
-        if TOTAL_TOKENS in token_usage_dict:
-            df[PROMPT_TOKENS] = int(token_usage_dict[TOTAL_TOKENS])
+            model_name = response.llm_output.get(MODEL_NAME, "")
 
-        if PROMPT_TOKENS in token_usage_dict:
-            df[TOTAL_TOKENS] = int(token_usage_dict[PROMPT_TOKENS])
+        prompt_responses = [
+            llmresult.generations[0][0].text for llmresult in flattened_llmresult
+        ]
 
-        if COMPLETION_TOKENS in token_usage_dict:
-            df[COMPLETION_TOKENS] = token_usage_dict[COMPLETION_TOKENS]
+        self._publish_events(
+            run_id,
+            prompt_responses,
+            run_duration,
+            SUCCESS,
+            model_name,
+            token_usage_dict,
+        )
 
-        df[MODEL_NAME] = model_name
-        df[RUN_ID] = str(run_id)
-        df[DURATION] = run_duration
+    def on_llm_error(self, error: BaseException, **kwargs: Any) -> None:
+        run_id = kwargs[RUN_ID]
+        duration = int(time.time()) - self.run_id_starttime[run_id]
 
-        try:
-            self.fiddler_client.publish_events_batch(self.project, self.model, df)
-        except Exception as e:
-            print(f"Error publishing events to fiddler: {e}. continuing...")  # noqa: T201
+        self._publish_events(
+            run_id, [""] * len(self.run_id_prompts[run_id]), duration, FAILURE
+        )