def run_detect_intent_queries()

in src/dfcx_scrapi/tools/evaluations.py [0:0]

• 65 lines of code
• 12 McCabe index (conditional complexity)

    def run_detect_intent_queries(self, df: pd.DataFrame) -> pd.DataFrame:
        for index, row in tqdm(df.iterrows(), total=df.shape[0]):
            data = {}
            if row["action_id"] == 1:
                self.session_id = self.sessions_client.build_session_id(
                    self.agent_id)
                data["session_id"] = self.session_id
                data["agent_id"] = self.agent_id

            else:
                data["session_id"] = self.session_id
                data["agent_id"] = self.agent_id

            # If the incoming dataset has an empty value in the row, skip it
            # this is because we build the incoming dataset with multi-row
            # actions to be able to evaluate `inner-loop` tasks
            if row["action_type"] != "User Utterance":
                continue

            session_parameters = None

            if "session_parameters" in row:
                session_parameters = self.str_to_dict(row["session_parameters"])

            res = self.sessions_client.detect_intent(
                agent_id=self.agent_id,
                session_id=self.session_id,
                text=row["action_input"],
                parameters=session_parameters
            )
            # Add data to the existing row
            df.loc[index, ["session_id", "agent_id"]] = [
                data["session_id"],
                data["agent_id"],
            ]
            text_res = self.ar._extract_text(res)

            # Handle Agent Responses
            if row["utterance_pair"] != "":
                utterance_idx = int(row["utterance_pair"])
                df.loc[utterance_idx, ["agent_response"]] = [text_res]

            else:
                # collect the data for inserting later
                self.unexpected_rows.append(
                    {
                        "session_id": data["session_id"],
                        "agent_id": data["agent_id"],
                        "action_type": "UNEXPECTED Agent Response",
                        "index": index,
                        "column": "agent_response",
                        "data": text_res
                    }
                    )

            # Handle Playbook Invocations
            playbook_responses = (
                self.sessions_client.collect_playbook_responses(res)
            )
            if len(playbook_responses) > 0:
                df = self.process_playbook_invocations(
                    playbook_responses, index, row, df
                )

            # Handle Flow Invocations
            flow_responses = self.sessions_client.collect_flow_responses(res)
            if len(flow_responses) > 0:
                df = self.process_flow_invocations(
                    flow_responses, index, row, df
                )

            # Handle Tool Invocations
            if "tool_call_quality" in self.user_input_metrics:
                tool_responses = (
                    self.sessions_client.collect_tool_responses(res)
                )
                if tool_responses:  # Only call if not empty
                    df = self.process_tool_invocations(
                        tool_responses,
                        index,
                        row,
                        df
                    )

        return df