Merge branch 'langgenius:main' into feat-add-remote-file-upload
GuanMu
1 year ago
committed by
GitHub
commit
fff6edf538
@ -0,0 +1,97 @@
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from typing import Any, Optional, Union
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from pydantic import BaseModel, Field, field_validator
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from pydantic_core.core_schema import ValidationInfo
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from core.ops.utils import replace_text_with_content
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class WeaveTokenUsage(BaseModel):
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input_tokens: Optional[int] = None
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output_tokens: Optional[int] = None
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total_tokens: Optional[int] = None
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class WeaveMultiModel(BaseModel):
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file_list: Optional[list[str]] = Field(None, description="List of files")
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class WeaveTraceModel(WeaveTokenUsage, WeaveMultiModel):
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id: str = Field(..., description="ID of the trace")
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op: str = Field(..., description="Name of the operation")
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inputs: Optional[Union[str, dict[str, Any], list, None]] = Field(None, description="Inputs of the trace")
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outputs: Optional[Union[str, dict[str, Any], list, None]] = Field(None, description="Outputs of the trace")
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attributes: Optional[Union[str, dict[str, Any], list, None]] = Field(
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None, description="Metadata and attributes associated with trace"
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)
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exception: Optional[str] = Field(None, description="Exception message of the trace")
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@field_validator("inputs", "outputs")
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@classmethod
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def ensure_dict(cls, v, info: ValidationInfo):
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field_name = info.field_name
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values = info.data
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if v == {} or v is None:
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return v
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usage_metadata = {
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"input_tokens": values.get("input_tokens", 0),
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"output_tokens": values.get("output_tokens", 0),
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"total_tokens": values.get("total_tokens", 0),
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}
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file_list = values.get("file_list", [])
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if isinstance(v, str):
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if field_name == "inputs":
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return {
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"messages": {
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"role": "user",
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"content": v,
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"usage_metadata": usage_metadata,
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"file_list": file_list,
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},
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}
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elif field_name == "outputs":
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return {
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"choices": {
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"role": "ai",
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"content": v,
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"usage_metadata": usage_metadata,
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"file_list": file_list,
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},
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}
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elif isinstance(v, list):
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data = {}
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if len(v) > 0 and isinstance(v[0], dict):
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# rename text to content
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v = replace_text_with_content(data=v)
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if field_name == "inputs":
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data = {
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"messages": [
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dict(msg, **{"usage_metadata": usage_metadata, "file_list": file_list}) for msg in v
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]
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if isinstance(v, list)
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else v,
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}
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elif field_name == "outputs":
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data = {
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"choices": {
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"role": "ai",
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"content": v,
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"usage_metadata": usage_metadata,
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"file_list": file_list,
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},
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}
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return data
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else:
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return {
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"choices": {
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"role": "ai" if field_name == "outputs" else "user",
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"content": str(v),
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"usage_metadata": usage_metadata,
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"file_list": file_list,
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},
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}
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if isinstance(v, dict):
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v["usage_metadata"] = usage_metadata
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v["file_list"] = file_list
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return v
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return v
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@ -0,0 +1,420 @@
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import json
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import logging
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import os
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import uuid
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from datetime import datetime, timedelta
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from typing import Any, Optional, cast
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import wandb
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import weave
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from core.ops.base_trace_instance import BaseTraceInstance
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from core.ops.entities.config_entity import WeaveConfig
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from core.ops.entities.trace_entity import (
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BaseTraceInfo,
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DatasetRetrievalTraceInfo,
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GenerateNameTraceInfo,
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MessageTraceInfo,
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ModerationTraceInfo,
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SuggestedQuestionTraceInfo,
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ToolTraceInfo,
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TraceTaskName,
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WorkflowTraceInfo,
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)
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from core.ops.weave_trace.entities.weave_trace_entity import WeaveTraceModel
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from extensions.ext_database import db
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from models.model import EndUser, MessageFile
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from models.workflow import WorkflowNodeExecution
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logger = logging.getLogger(__name__)
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class WeaveDataTrace(BaseTraceInstance):
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def __init__(
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self,
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weave_config: WeaveConfig,
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):
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super().__init__(weave_config)
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self.weave_api_key = weave_config.api_key
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self.project_name = weave_config.project
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self.entity = weave_config.entity
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# Login with API key first
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login_status = wandb.login(key=self.weave_api_key, verify=True, relogin=True)
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if not login_status:
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logger.error("Failed to login to Weights & Biases with the provided API key")
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raise ValueError("Weave login failed")
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# Then initialize weave client
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self.weave_client = weave.init(
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project_name=(f"{self.entity}/{self.project_name}" if self.entity else self.project_name)
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)
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self.file_base_url = os.getenv("FILES_URL", "http://127.0.0.1:5001")
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self.calls: dict[str, Any] = {}
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def get_project_url(
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self,
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):
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try:
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project_url = f"https://wandb.ai/{self.weave_client._project_id()}"
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return project_url
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except Exception as e:
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logger.debug(f"Weave get run url failed: {str(e)}")
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raise ValueError(f"Weave get run url failed: {str(e)}")
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def trace(self, trace_info: BaseTraceInfo):
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logger.debug(f"Trace info: {trace_info}")
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if isinstance(trace_info, WorkflowTraceInfo):
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self.workflow_trace(trace_info)
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if isinstance(trace_info, MessageTraceInfo):
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self.message_trace(trace_info)
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if isinstance(trace_info, ModerationTraceInfo):
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self.moderation_trace(trace_info)
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if isinstance(trace_info, SuggestedQuestionTraceInfo):
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self.suggested_question_trace(trace_info)
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if isinstance(trace_info, DatasetRetrievalTraceInfo):
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self.dataset_retrieval_trace(trace_info)
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if isinstance(trace_info, ToolTraceInfo):
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self.tool_trace(trace_info)
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if isinstance(trace_info, GenerateNameTraceInfo):
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self.generate_name_trace(trace_info)
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def workflow_trace(self, trace_info: WorkflowTraceInfo):
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trace_id = trace_info.message_id or trace_info.workflow_run_id
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if trace_info.start_time is None:
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trace_info.start_time = datetime.now()
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if trace_info.message_id:
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message_attributes = trace_info.metadata
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message_attributes["workflow_app_log_id"] = trace_info.workflow_app_log_id
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message_attributes["message_id"] = trace_info.message_id
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message_attributes["workflow_run_id"] = trace_info.workflow_run_id
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message_attributes["trace_id"] = trace_id
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message_attributes["start_time"] = trace_info.start_time
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message_attributes["end_time"] = trace_info.end_time
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message_attributes["tags"] = ["message", "workflow"]
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message_run = WeaveTraceModel(
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id=trace_info.message_id,
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op=str(TraceTaskName.MESSAGE_TRACE.value),
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inputs=dict(trace_info.workflow_run_inputs),
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outputs=dict(trace_info.workflow_run_outputs),
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total_tokens=trace_info.total_tokens,
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attributes=message_attributes,
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exception=trace_info.error,
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file_list=[],
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)
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self.start_call(message_run, parent_run_id=trace_info.workflow_run_id)
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self.finish_call(message_run)
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workflow_attributes = trace_info.metadata
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workflow_attributes["workflow_run_id"] = trace_info.workflow_run_id
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workflow_attributes["trace_id"] = trace_id
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workflow_attributes["start_time"] = trace_info.start_time
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workflow_attributes["end_time"] = trace_info.end_time
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workflow_attributes["tags"] = ["workflow"]
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workflow_run = WeaveTraceModel(
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file_list=trace_info.file_list,
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total_tokens=trace_info.total_tokens,
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id=trace_info.workflow_run_id,
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op=str(TraceTaskName.WORKFLOW_TRACE.value),
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inputs=dict(trace_info.workflow_run_inputs),
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outputs=dict(trace_info.workflow_run_outputs),
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attributes=workflow_attributes,
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exception=trace_info.error,
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)
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self.start_call(workflow_run, parent_run_id=trace_info.message_id)
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# through workflow_run_id get all_nodes_execution
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workflow_nodes_execution_id_records = (
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db.session.query(WorkflowNodeExecution.id)
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.filter(WorkflowNodeExecution.workflow_run_id == trace_info.workflow_run_id)
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.all()
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)
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for node_execution_id_record in workflow_nodes_execution_id_records:
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node_execution = (
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db.session.query(
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WorkflowNodeExecution.id,
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WorkflowNodeExecution.tenant_id,
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WorkflowNodeExecution.app_id,
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WorkflowNodeExecution.title,
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|
WorkflowNodeExecution.node_type,
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|
WorkflowNodeExecution.status,
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|
WorkflowNodeExecution.inputs,
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|
WorkflowNodeExecution.outputs,
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|
WorkflowNodeExecution.created_at,
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|
WorkflowNodeExecution.elapsed_time,
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|
WorkflowNodeExecution.process_data,
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|
WorkflowNodeExecution.execution_metadata,
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|
)
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|
.filter(WorkflowNodeExecution.id == node_execution_id_record.id)
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.first()
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)
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|
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if not node_execution:
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continue
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|
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node_execution_id = node_execution.id
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tenant_id = node_execution.tenant_id
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app_id = node_execution.app_id
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node_name = node_execution.title
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node_type = node_execution.node_type
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status = node_execution.status
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|
if node_type == "llm":
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inputs = (
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json.loads(node_execution.process_data).get("prompts", {}) if node_execution.process_data else {}
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|
)
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|
else:
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|
inputs = json.loads(node_execution.inputs) if node_execution.inputs else {}
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|
outputs = json.loads(node_execution.outputs) if node_execution.outputs else {}
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|
created_at = node_execution.created_at or datetime.now()
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|
elapsed_time = node_execution.elapsed_time
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|
finished_at = created_at + timedelta(seconds=elapsed_time)
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|
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|
execution_metadata = (
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|
json.loads(node_execution.execution_metadata) if node_execution.execution_metadata else {}
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|
)
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|
node_total_tokens = execution_metadata.get("total_tokens", 0)
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|
attributes = execution_metadata.copy()
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|
attributes.update(
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|
{
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|
"workflow_run_id": trace_info.workflow_run_id,
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|
"node_execution_id": node_execution_id,
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|
"tenant_id": tenant_id,
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|
"app_id": app_id,
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|
"app_name": node_name,
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|
"node_type": node_type,
|
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|
"status": status,
|
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|
}
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|
)
|
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|
|
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|
process_data = json.loads(node_execution.process_data) if node_execution.process_data else {}
|
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|
if process_data and process_data.get("model_mode") == "chat":
|
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|
attributes.update(
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|
{
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|
"ls_provider": process_data.get("model_provider", ""),
|
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|
"ls_model_name": process_data.get("model_name", ""),
|
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|
}
|
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|
)
|
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|
attributes["tags"] = ["node_execution"]
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|
attributes["start_time"] = created_at
|
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|
attributes["end_time"] = finished_at
|
||||||
|
attributes["elapsed_time"] = elapsed_time
|
||||||
|
attributes["workflow_run_id"] = trace_info.workflow_run_id
|
||||||
|
attributes["trace_id"] = trace_id
|
||||||
|
node_run = WeaveTraceModel(
|
||||||
|
total_tokens=node_total_tokens,
|
||||||
|
op=node_type,
|
||||||
|
inputs=inputs,
|
||||||
|
outputs=outputs,
|
||||||
|
file_list=trace_info.file_list,
|
||||||
|
attributes=attributes,
|
||||||
|
id=node_execution_id,
|
||||||
|
exception=None,
|
||||||
|
)
|
||||||
|
|
||||||
|
self.start_call(node_run, parent_run_id=trace_info.workflow_run_id)
|
||||||
|
self.finish_call(node_run)
|
||||||
|
|
||||||
|
self.finish_call(workflow_run)
|
||||||
|
|
||||||
|
def message_trace(self, trace_info: MessageTraceInfo):
|
||||||
|
# get message file data
|
||||||
|
file_list = cast(list[str], trace_info.file_list) or []
|
||||||
|
message_file_data: Optional[MessageFile] = trace_info.message_file_data
|
||||||
|
file_url = f"{self.file_base_url}/{message_file_data.url}" if message_file_data else ""
|
||||||
|
file_list.append(file_url)
|
||||||
|
attributes = trace_info.metadata
|
||||||
|
message_data = trace_info.message_data
|
||||||
|
if message_data is None:
|
||||||
|
return
|
||||||
|
message_id = message_data.id
|
||||||
|
|
||||||
|
user_id = message_data.from_account_id
|
||||||
|
attributes["user_id"] = user_id
|
||||||
|
|
||||||
|
if message_data.from_end_user_id:
|
||||||
|
end_user_data: Optional[EndUser] = (
|
||||||
|
db.session.query(EndUser).filter(EndUser.id == message_data.from_end_user_id).first()
|
||||||
|
)
|
||||||
|
if end_user_data is not None:
|
||||||
|
end_user_id = end_user_data.session_id
|
||||||
|
attributes["end_user_id"] = end_user_id
|
||||||
|
|
||||||
|
attributes["message_id"] = message_id
|
||||||
|
attributes["start_time"] = trace_info.start_time
|
||||||
|
attributes["end_time"] = trace_info.end_time
|
||||||
|
attributes["tags"] = ["message", str(trace_info.conversation_mode)]
|
||||||
|
message_run = WeaveTraceModel(
|
||||||
|
id=message_id,
|
||||||
|
op=str(TraceTaskName.MESSAGE_TRACE.value),
|
||||||
|
input_tokens=trace_info.message_tokens,
|
||||||
|
output_tokens=trace_info.answer_tokens,
|
||||||
|
total_tokens=trace_info.total_tokens,
|
||||||
|
inputs=trace_info.inputs,
|
||||||
|
outputs=trace_info.outputs,
|
||||||
|
exception=trace_info.error,
|
||||||
|
file_list=file_list,
|
||||||
|
attributes=attributes,
|
||||||
|
)
|
||||||
|
self.start_call(message_run)
|
||||||
|
|
||||||
|
# create llm run parented to message run
|
||||||
|
llm_run = WeaveTraceModel(
|
||||||
|
id=str(uuid.uuid4()),
|
||||||
|
input_tokens=trace_info.message_tokens,
|
||||||
|
output_tokens=trace_info.answer_tokens,
|
||||||
|
total_tokens=trace_info.total_tokens,
|
||||||
|
op="llm",
|
||||||
|
inputs=trace_info.inputs,
|
||||||
|
outputs=trace_info.outputs,
|
||||||
|
attributes=attributes,
|
||||||
|
file_list=[],
|
||||||
|
exception=None,
|
||||||
|
)
|
||||||
|
self.start_call(
|
||||||
|
llm_run,
|
||||||
|
parent_run_id=message_id,
|
||||||
|
)
|
||||||
|
self.finish_call(llm_run)
|
||||||
|
self.finish_call(message_run)
|
||||||
|
|
||||||
|
def moderation_trace(self, trace_info: ModerationTraceInfo):
|
||||||
|
if trace_info.message_data is None:
|
||||||
|
return
|
||||||
|
|
||||||
|
attributes = trace_info.metadata
|
||||||
|
attributes["tags"] = ["moderation"]
|
||||||
|
attributes["message_id"] = trace_info.message_id
|
||||||
|
attributes["start_time"] = trace_info.start_time or trace_info.message_data.created_at
|
||||||
|
attributes["end_time"] = trace_info.end_time or trace_info.message_data.updated_at
|
||||||
|
|
||||||
|
moderation_run = WeaveTraceModel(
|
||||||
|
id=str(uuid.uuid4()),
|
||||||
|
op=str(TraceTaskName.MODERATION_TRACE.value),
|
||||||
|
inputs=trace_info.inputs,
|
||||||
|
outputs={
|
||||||
|
"action": trace_info.action,
|
||||||
|
"flagged": trace_info.flagged,
|
||||||
|
"preset_response": trace_info.preset_response,
|
||||||
|
"inputs": trace_info.inputs,
|
||||||
|
},
|
||||||
|
attributes=attributes,
|
||||||
|
exception=getattr(trace_info, "error", None),
|
||||||
|
file_list=[],
|
||||||
|
)
|
||||||
|
self.start_call(moderation_run, parent_run_id=trace_info.message_id)
|
||||||
|
self.finish_call(moderation_run)
|
||||||
|
|
||||||
|
def suggested_question_trace(self, trace_info: SuggestedQuestionTraceInfo):
|
||||||
|
message_data = trace_info.message_data
|
||||||
|
if message_data is None:
|
||||||
|
return
|
||||||
|
attributes = trace_info.metadata
|
||||||
|
attributes["message_id"] = trace_info.message_id
|
||||||
|
attributes["tags"] = ["suggested_question"]
|
||||||
|
attributes["start_time"] = (trace_info.start_time or message_data.created_at,)
|
||||||
|
attributes["end_time"] = (trace_info.end_time or message_data.updated_at,)
|
||||||
|
|
||||||
|
suggested_question_run = WeaveTraceModel(
|
||||||
|
id=str(uuid.uuid4()),
|
||||||
|
op=str(TraceTaskName.SUGGESTED_QUESTION_TRACE.value),
|
||||||
|
inputs=trace_info.inputs,
|
||||||
|
outputs=trace_info.suggested_question,
|
||||||
|
attributes=attributes,
|
||||||
|
exception=trace_info.error,
|
||||||
|
file_list=[],
|
||||||
|
)
|
||||||
|
|
||||||
|
self.start_call(suggested_question_run, parent_run_id=trace_info.message_id)
|
||||||
|
self.finish_call(suggested_question_run)
|
||||||
|
|
||||||
|
def dataset_retrieval_trace(self, trace_info: DatasetRetrievalTraceInfo):
|
||||||
|
if trace_info.message_data is None:
|
||||||
|
return
|
||||||
|
attributes = trace_info.metadata
|
||||||
|
attributes["message_id"] = trace_info.message_id
|
||||||
|
attributes["tags"] = ["dataset_retrieval"]
|
||||||
|
attributes["start_time"] = (trace_info.start_time or trace_info.message_data.created_at,)
|
||||||
|
attributes["end_time"] = (trace_info.end_time or trace_info.message_data.updated_at,)
|
||||||
|
|
||||||
|
dataset_retrieval_run = WeaveTraceModel(
|
||||||
|
id=str(uuid.uuid4()),
|
||||||
|
op=str(TraceTaskName.DATASET_RETRIEVAL_TRACE.value),
|
||||||
|
inputs=trace_info.inputs,
|
||||||
|
outputs={"documents": trace_info.documents},
|
||||||
|
attributes=attributes,
|
||||||
|
exception=getattr(trace_info, "error", None),
|
||||||
|
file_list=[],
|
||||||
|
)
|
||||||
|
|
||||||
|
self.start_call(dataset_retrieval_run, parent_run_id=trace_info.message_id)
|
||||||
|
self.finish_call(dataset_retrieval_run)
|
||||||
|
|
||||||
|
def tool_trace(self, trace_info: ToolTraceInfo):
|
||||||
|
attributes = trace_info.metadata
|
||||||
|
attributes["tags"] = ["tool", trace_info.tool_name]
|
||||||
|
attributes["start_time"] = trace_info.start_time
|
||||||
|
attributes["end_time"] = trace_info.end_time
|
||||||
|
|
||||||
|
tool_run = WeaveTraceModel(
|
||||||
|
id=str(uuid.uuid4()),
|
||||||
|
op=trace_info.tool_name,
|
||||||
|
inputs=trace_info.tool_inputs,
|
||||||
|
outputs=trace_info.tool_outputs,
|
||||||
|
file_list=[cast(str, trace_info.file_url)] if trace_info.file_url else [],
|
||||||
|
attributes=attributes,
|
||||||
|
exception=trace_info.error,
|
||||||
|
)
|
||||||
|
message_id = trace_info.message_id or getattr(trace_info, "conversation_id", None)
|
||||||
|
message_id = message_id or None
|
||||||
|
self.start_call(tool_run, parent_run_id=message_id)
|
||||||
|
self.finish_call(tool_run)
|
||||||
|
|
||||||
|
def generate_name_trace(self, trace_info: GenerateNameTraceInfo):
|
||||||
|
attributes = trace_info.metadata
|
||||||
|
attributes["tags"] = ["generate_name"]
|
||||||
|
attributes["start_time"] = trace_info.start_time
|
||||||
|
attributes["end_time"] = trace_info.end_time
|
||||||
|
|
||||||
|
name_run = WeaveTraceModel(
|
||||||
|
id=str(uuid.uuid4()),
|
||||||
|
op=str(TraceTaskName.GENERATE_NAME_TRACE.value),
|
||||||
|
inputs=trace_info.inputs,
|
||||||
|
outputs=trace_info.outputs,
|
||||||
|
attributes=attributes,
|
||||||
|
exception=getattr(trace_info, "error", None),
|
||||||
|
file_list=[],
|
||||||
|
)
|
||||||
|
|
||||||
|
self.start_call(name_run)
|
||||||
|
self.finish_call(name_run)
|
||||||
|
|
||||||
|
def api_check(self):
|
||||||
|
try:
|
||||||
|
login_status = wandb.login(key=self.weave_api_key, verify=True, relogin=True)
|
||||||
|
if not login_status:
|
||||||
|
raise ValueError("Weave login failed")
|
||||||
|
else:
|
||||||
|
print("Weave login successful")
|
||||||
|
return True
|
||||||
|
except Exception as e:
|
||||||
|
logger.debug(f"Weave API check failed: {str(e)}")
|
||||||
|
raise ValueError(f"Weave API check failed: {str(e)}")
|
||||||
|
|
||||||
|
def start_call(self, run_data: WeaveTraceModel, parent_run_id: Optional[str] = None):
|
||||||
|
call = self.weave_client.create_call(op=run_data.op, inputs=run_data.inputs, attributes=run_data.attributes)
|
||||||
|
self.calls[run_data.id] = call
|
||||||
|
if parent_run_id:
|
||||||
|
self.calls[run_data.id].parent_id = parent_run_id
|
||||||
|
|
||||||
|
def finish_call(self, run_data: WeaveTraceModel):
|
||||||
|
call = self.calls.get(run_data.id)
|
||||||
|
if call:
|
||||||
|
self.weave_client.finish_call(call=call, output=run_data.outputs, exception=run_data.exception)
|
||||||
|
else:
|
||||||
|
raise ValueError(f"Call with id {run_data.id} not found")
|
||||||
File diff suppressed because one or more lines are too long
|
After Width: | Height: | Size: 68 KiB |
File diff suppressed because one or more lines are too long
|
After Width: | Height: | Size: 68 KiB |
File diff suppressed because one or more lines are too long
@ -0,0 +1,16 @@
|
|||||||
|
// GENERATE BY script
|
||||||
|
// DON NOT EDIT IT MANUALLY
|
||||||
|
|
||||||
|
import * as React from 'react'
|
||||||
|
import data from './WeaveIcon.json'
|
||||||
|
import IconBase from '@/app/components/base/icons/IconBase'
|
||||||
|
import type { IconBaseProps, IconData } from '@/app/components/base/icons/IconBase'
|
||||||
|
|
||||||
|
const Icon = React.forwardRef<React.MutableRefObject<SVGElement>, Omit<IconBaseProps, 'data'>>((
|
||||||
|
props,
|
||||||
|
ref,
|
||||||
|
) => <IconBase {...props} ref={ref} data={data as IconData} />)
|
||||||
|
|
||||||
|
Icon.displayName = 'WeaveIcon'
|
||||||
|
|
||||||
|
export default Icon
|
||||||
File diff suppressed because one or more lines are too long
@ -0,0 +1,16 @@
|
|||||||
|
// GENERATE BY script
|
||||||
|
// DON NOT EDIT IT MANUALLY
|
||||||
|
|
||||||
|
import * as React from 'react'
|
||||||
|
import data from './WeaveIconBig.json'
|
||||||
|
import IconBase from '@/app/components/base/icons/IconBase'
|
||||||
|
import type { IconBaseProps, IconData } from '@/app/components/base/icons/IconBase'
|
||||||
|
|
||||||
|
const Icon = React.forwardRef<React.MutableRefObject<SVGElement>, Omit<IconBaseProps, 'data'>>((
|
||||||
|
props,
|
||||||
|
ref,
|
||||||
|
) => <IconBase {...props} ref={ref} data={data as IconData} />)
|
||||||
|
|
||||||
|
Icon.displayName = 'WeaveIconBig'
|
||||||
|
|
||||||
|
export default Icon
|
||||||
Loading…
Reference in New Issue