tjdify/api/core/ops/langfuse_trace/langfuse_trace.py

import json
import logging
import os
from datetime import datetime, timedelta
from typing import Optional

from langfuse import Langfuse

from core.ops.base_trace_instance import BaseTraceInstance
from core.ops.entities.config_entity import LangfuseConfig
from core.ops.entities.trace_entity import (
    BaseTraceInfo,
    DatasetRetrievalTraceInfo,
    GenerateNameTraceInfo,
    MessageTraceInfo,
    ModerationTraceInfo,
    SuggestedQuestionTraceInfo,
    ToolTraceInfo,
    WorkflowTraceInfo,
)
from core.ops.langfuse_trace.entities.langfuse_trace_entity import (
    GenerationUsage,
    LangfuseGeneration,
    LangfuseSpan,
    LangfuseTrace,
    LevelEnum,
    UnitEnum,
)
from core.ops.utils import filter_none_values
from extensions.ext_database import db
from models.model import EndUser
from models.workflow import WorkflowNodeExecution

logger = logging.getLogger(__name__)


class LangFuseDataTrace(BaseTraceInstance):
    def __init__(
        self,
        langfuse_config: LangfuseConfig,
    ):
        super().__init__(langfuse_config)
        self.langfuse_client = Langfuse(
            public_key=langfuse_config.public_key,
            secret_key=langfuse_config.secret_key,
            host=langfuse_config.host,
        )
        self.file_base_url = os.getenv("FILES_URL", "http://127.0.0.1:5001")

    def trace(self, trace_info: BaseTraceInfo):
        if isinstance(trace_info, WorkflowTraceInfo):
            self.workflow_trace(trace_info)
        if isinstance(trace_info, MessageTraceInfo):
            self.message_trace(trace_info)
        if isinstance(trace_info, ModerationTraceInfo):
            self.moderation_trace(trace_info)
        if isinstance(trace_info, SuggestedQuestionTraceInfo):
            self.suggested_question_trace(trace_info)
        if isinstance(trace_info, DatasetRetrievalTraceInfo):
            self.dataset_retrieval_trace(trace_info)
        if isinstance(trace_info, ToolTraceInfo):
            self.tool_trace(trace_info)
        if isinstance(trace_info, GenerateNameTraceInfo):
            self.generate_name_trace(trace_info)

    def workflow_trace(self, trace_info: WorkflowTraceInfo):
        trace_id = trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id
        user_id = trace_info.metadata.get("user_id")
        if trace_info.message_id:
            trace_id = trace_info.message_id
            name = f"message_{trace_info.message_id}"
            trace_data = LangfuseTrace(
                id=trace_info.message_id,
                user_id=user_id,
                name=name,
                input=trace_info.workflow_run_inputs,
                output=trace_info.workflow_run_outputs,
                metadata=trace_info.metadata,
                session_id=trace_info.conversation_id,
                tags=["message", "workflow"],
            )
            self.add_trace(langfuse_trace_data=trace_data)
            workflow_span_data = LangfuseSpan(
                id=trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id,
                name=f"workflow_{trace_info.workflow_app_log_id}" if trace_info.workflow_app_log_id else f"workflow_{trace_info.workflow_run_id}",
                input=trace_info.workflow_run_inputs,
                output=trace_info.workflow_run_outputs,
                trace_id=trace_id,
                start_time=trace_info.start_time,
                end_time=trace_info.end_time,
                metadata=trace_info.metadata,
                level=LevelEnum.DEFAULT if trace_info.error == "" else LevelEnum.ERROR,
                status_message=trace_info.error if trace_info.error else "",
            )
            self.add_span(langfuse_span_data=workflow_span_data)
        else:
            trace_data = LangfuseTrace(
                id=trace_id,
                user_id=user_id,
                name=f"workflow_{trace_info.workflow_app_log_id}" if trace_info.workflow_app_log_id else f"workflow_{trace_info.workflow_run_id}",
                input=trace_info.workflow_run_inputs,
                output=trace_info.workflow_run_outputs,
                metadata=trace_info.metadata,
                session_id=trace_info.conversation_id,
                tags=["workflow"],
            )
            self.add_trace(langfuse_trace_data=trace_data)

        # through workflow_run_id get all_nodes_execution
        workflow_nodes_executions = (
            db.session.query(
                WorkflowNodeExecution.id,
                WorkflowNodeExecution.tenant_id,
                WorkflowNodeExecution.app_id,
                WorkflowNodeExecution.title,
                WorkflowNodeExecution.node_type,
                WorkflowNodeExecution.status,
                WorkflowNodeExecution.inputs,
                WorkflowNodeExecution.outputs,
                WorkflowNodeExecution.created_at,
                WorkflowNodeExecution.elapsed_time,
                WorkflowNodeExecution.process_data,
                WorkflowNodeExecution.execution_metadata,
            )
            .filter(WorkflowNodeExecution.workflow_run_id == trace_info.workflow_run_id)
            .all()
        )

        for node_execution in workflow_nodes_executions:
            node_execution_id = node_execution.id
            tenant_id = node_execution.tenant_id
            app_id = node_execution.app_id
            node_name = node_execution.title
            node_type = node_execution.node_type
            status = node_execution.status
            if node_type == "llm":
                inputs = json.loads(node_execution.process_data).get(
                    "prompts", {}
                    ) if node_execution.process_data else {}
            else:
                inputs = json.loads(node_execution.inputs) if node_execution.inputs else {}
            outputs = (
                json.loads(node_execution.outputs) if node_execution.outputs else {}
            )
            created_at = node_execution.created_at if node_execution.created_at else datetime.now()
            elapsed_time = node_execution.elapsed_time
            finished_at = created_at + timedelta(seconds=elapsed_time)

            metadata = json.loads(node_execution.execution_metadata) if node_execution.execution_metadata else {}
            metadata.update(
                {
                    "workflow_run_id": trace_info.workflow_run_id,
                    "node_execution_id": node_execution_id,
                    "tenant_id": tenant_id,
                    "app_id": app_id,
                    "node_name": node_name,
                    "node_type": node_type,
                    "status": status,
                }
            )

            # add span
            if trace_info.message_id:
                span_data = LangfuseSpan(
                    id=node_execution_id,
                    name=f"{node_name}_{node_execution_id}",
                    input=inputs,
                    output=outputs,
                    trace_id=trace_id,
                    start_time=created_at,
                    end_time=finished_at,
                    metadata=metadata,
                    level=LevelEnum.DEFAULT if status == 'succeeded' else LevelEnum.ERROR,
                    status_message=trace_info.error if trace_info.error else "",
                    parent_observation_id=trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id,
                )
            else:
                span_data = LangfuseSpan(
                    id=node_execution_id,
                    name=f"{node_name}_{node_execution_id}",
                    input=inputs,
                    output=outputs,
                    trace_id=trace_id,
                    start_time=created_at,
                    end_time=finished_at,
                    metadata=metadata,
                    level=LevelEnum.DEFAULT if status == 'succeeded' else LevelEnum.ERROR,
                    status_message=trace_info.error if trace_info.error else "",
                )

            self.add_span(langfuse_span_data=span_data)

            process_data = json.loads(node_execution.process_data) if node_execution.process_data else {}
            if process_data and process_data.get("model_mode") == "chat":
                total_token = metadata.get("total_tokens", 0)
                # add generation
                generation_usage = GenerationUsage(
                    totalTokens=total_token,
                )

                node_generation_data = LangfuseGeneration(
                    name=f"generation_{node_execution_id}",
                    trace_id=trace_id,
                    parent_observation_id=node_execution_id,
                    start_time=created_at,
                    end_time=finished_at,
                    input=inputs,
                    output=outputs,
                    metadata=metadata,
                    level=LevelEnum.DEFAULT if status == 'succeeded' else LevelEnum.ERROR,
                    status_message=trace_info.error if trace_info.error else "",
                    usage=generation_usage,
                )

                self.add_generation(langfuse_generation_data=node_generation_data)

    def message_trace(
        self, trace_info: MessageTraceInfo, **kwargs
    ):
        # get message file data
        file_list = trace_info.file_list
        metadata = trace_info.metadata
        message_data = trace_info.message_data
        message_id = message_data.id

        user_id = message_data.from_account_id
        if message_data.from_end_user_id:
            end_user_data: EndUser = db.session.query(EndUser).filter(
                EndUser.id == message_data.from_end_user_id
            ).first()
            if end_user_data is not None:
                user_id = end_user_data.session_id
                metadata["user_id"] = user_id

        trace_data = LangfuseTrace(
            id=message_id,
            user_id=user_id,
            name=f"message_{message_id}",
            input={
                "message": trace_info.inputs,
                "files": file_list,
                "message_tokens": trace_info.message_tokens,
                "answer_tokens": trace_info.answer_tokens,
                "total_tokens": trace_info.total_tokens,
                "error": trace_info.error,
                "provider_response_latency": message_data.provider_response_latency,
                "created_at": trace_info.start_time,
            },
            output=trace_info.outputs,
            metadata=metadata,
            session_id=message_data.conversation_id,
            tags=["message", str(trace_info.conversation_mode)],
            version=None,
            release=None,
            public=None,
        )
        self.add_trace(langfuse_trace_data=trace_data)

        # start add span
        generation_usage = GenerationUsage(
            totalTokens=trace_info.total_tokens,
            input=trace_info.message_tokens,
            output=trace_info.answer_tokens,
            total=trace_info.total_tokens,
            unit=UnitEnum.TOKENS,
            totalCost=message_data.total_price,
        )

        langfuse_generation_data = LangfuseGeneration(
            name=f"generation_{message_id}",
            trace_id=message_id,
            start_time=trace_info.start_time,
            end_time=trace_info.end_time,
            model=message_data.model_id,
            input=trace_info.inputs,
            output=message_data.answer,
            metadata=metadata,
            level=LevelEnum.DEFAULT if message_data.status != 'error' else LevelEnum.ERROR,
            status_message=message_data.error if message_data.error else "",
            usage=generation_usage,
        )

        self.add_generation(langfuse_generation_data)

    def moderation_trace(self, trace_info: ModerationTraceInfo):
        span_data = LangfuseSpan(
            name="moderation",
            input=trace_info.inputs,
            output={
                "action": trace_info.action,
                "flagged": trace_info.flagged,
                "preset_response": trace_info.preset_response,
                "inputs": trace_info.inputs,
            },
            trace_id=trace_info.message_id,
            start_time=trace_info.start_time or trace_info.message_data.created_at,
            end_time=trace_info.end_time or trace_info.message_data.created_at,
            metadata=trace_info.metadata,
        )

        self.add_span(langfuse_span_data=span_data)

    def suggested_question_trace(self, trace_info: SuggestedQuestionTraceInfo):
        message_data = trace_info.message_data
        generation_usage = GenerationUsage(
            totalTokens=len(str(trace_info.suggested_question)),
            input=len(trace_info.inputs),
            output=len(trace_info.suggested_question),
            total=len(trace_info.suggested_question),
            unit=UnitEnum.CHARACTERS,
        )

        generation_data = LangfuseGeneration(
            name="suggested_question",
            input=trace_info.inputs,
            output=str(trace_info.suggested_question),
            trace_id=trace_info.message_id,
            start_time=trace_info.start_time,
            end_time=trace_info.end_time,
            metadata=trace_info.metadata,
            level=LevelEnum.DEFAULT if message_data.status != 'error' else LevelEnum.ERROR,
            status_message=message_data.error if message_data.error else "",
            usage=generation_usage,
        )

        self.add_generation(langfuse_generation_data=generation_data)

    def dataset_retrieval_trace(self, trace_info: DatasetRetrievalTraceInfo):
        dataset_retrieval_span_data = LangfuseSpan(
            name="dataset_retrieval",
            input=trace_info.inputs,
            output={"documents": trace_info.documents},
            trace_id=trace_info.message_id,
            start_time=trace_info.start_time or trace_info.message_data.created_at,
            end_time=trace_info.end_time or trace_info.message_data.updated_at,
            metadata=trace_info.metadata,
        )

        self.add_span(langfuse_span_data=dataset_retrieval_span_data)

    def tool_trace(self, trace_info: ToolTraceInfo):
        tool_span_data = LangfuseSpan(
            name=trace_info.tool_name,
            input=trace_info.tool_inputs,
            output=trace_info.tool_outputs,
            trace_id=trace_info.message_id,
            start_time=trace_info.start_time,
            end_time=trace_info.end_time,
            metadata=trace_info.metadata,
            level=LevelEnum.DEFAULT if trace_info.error == "" or trace_info.error is None else LevelEnum.ERROR,
            status_message=trace_info.error,
        )

        self.add_span(langfuse_span_data=tool_span_data)

    def generate_name_trace(self, trace_info: GenerateNameTraceInfo):
        name_generation_trace_data = LangfuseTrace(
            name="generate_name",
            input=trace_info.inputs,
            output=trace_info.outputs,
            user_id=trace_info.tenant_id,
            metadata=trace_info.metadata,
            session_id=trace_info.conversation_id,
        )

        self.add_trace(langfuse_trace_data=name_generation_trace_data)

        name_generation_span_data = LangfuseSpan(
            name="generate_name",
            input=trace_info.inputs,
            output=trace_info.outputs,
            trace_id=trace_info.conversation_id,
            start_time=trace_info.start_time,
            end_time=trace_info.end_time,
            metadata=trace_info.metadata,
        )
        self.add_span(langfuse_span_data=name_generation_span_data)

    def add_trace(self, langfuse_trace_data: Optional[LangfuseTrace] = None):
        format_trace_data = (
            filter_none_values(langfuse_trace_data.model_dump()) if langfuse_trace_data else {}
        )
        try:
            self.langfuse_client.trace(**format_trace_data)
            logger.debug("LangFuse Trace created successfully")
        except Exception as e:
            raise ValueError(f"LangFuse Failed to create trace: {str(e)}")

    def add_span(self, langfuse_span_data: Optional[LangfuseSpan] = None):
        format_span_data = (
            filter_none_values(langfuse_span_data.model_dump()) if langfuse_span_data else {}
        )
        try:
            self.langfuse_client.span(**format_span_data)
            logger.debug("LangFuse Span created successfully")
        except Exception as e:
            raise ValueError(f"LangFuse Failed to create span: {str(e)}")

    def update_span(self, span, langfuse_span_data: Optional[LangfuseSpan] = None):
        format_span_data = (
            filter_none_values(langfuse_span_data.model_dump()) if langfuse_span_data else {}
        )

        span.end(**format_span_data)

    def add_generation(
        self, langfuse_generation_data: Optional[LangfuseGeneration] = None
    ):
        format_generation_data = (
            filter_none_values(langfuse_generation_data.model_dump())
            if langfuse_generation_data
            else {}
        )
        try:
            self.langfuse_client.generation(**format_generation_data)
            logger.debug("LangFuse Generation created successfully")
        except Exception as e:
            raise ValueError(f"LangFuse Failed to create generation: {str(e)}")

    def update_generation(
        self, generation, langfuse_generation_data: Optional[LangfuseGeneration] = None
    ):
        format_generation_data = (
            filter_none_values(langfuse_generation_data.model_dump())
            if langfuse_generation_data
            else {}
        )

        generation.end(**format_generation_data)

    def api_check(self):
        try:
            return self.langfuse_client.auth_check()
        except Exception as e:
            logger.debug(f"LangFuse API check failed: {str(e)}")
            raise ValueError(f"LangFuse API check failed: {str(e)}")