Rm RunTypeEnum (#8553)

We already support raw strings in the SDK but would like to deprecate
client-side validation of run types. This removes its usage

libs/langchain/langchain/callbacks/tracers/base.py

@@ -10,7 +10,7 @@
 from tenacity import RetryCallState
 
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.callbacks.tracers.schemas import Run, RunTypeEnum
+from langchain.callbacks.tracers.schemas import Run
 from langchain.load.dump import dumpd
 from langchain.schema.document import Document
 from langchain.schema.output import ChatGeneration, LLMResult
@@ -110,7 +110,7 @@ def on_llm_start(
             start_time=start_time,
             execution_order=execution_order,
             child_execution_order=execution_order,
-            run_type=RunTypeEnum.llm,
+            run_type="llm",
             tags=tags or [],
         )
         self._start_trace(llm_run)
@@ -130,7 +130,7 @@ def on_llm_new_token(
 
         run_id_ = str(run_id)
         llm_run = self.run_map.get(run_id_)
-        if llm_run is None or llm_run.run_type != RunTypeEnum.llm:
+        if llm_run is None or llm_run.run_type != "llm":
             raise TracerException("No LLM Run found to be traced")
         llm_run.events.append(
             {
@@ -182,7 +182,7 @@ def on_llm_end(self, response: LLMResult, *, run_id: UUID, **kwargs: Any) -> Non
 
         run_id_ = str(run_id)
         llm_run = self.run_map.get(run_id_)
-        if llm_run is None or llm_run.run_type != RunTypeEnum.llm:
+        if llm_run is None or llm_run.run_type != "llm":
             raise TracerException("No LLM Run found to be traced")
         llm_run.outputs = response.dict()
         for i, generations in enumerate(response.generations):
@@ -210,7 +210,7 @@ def on_llm_error(
 
         run_id_ = str(run_id)
         llm_run = self.run_map.get(run_id_)
-        if llm_run is None or llm_run.run_type != RunTypeEnum.llm:
+        if llm_run is None or llm_run.run_type != "llm":
             raise TracerException("No LLM Run found to be traced")
         llm_run.error = repr(error)
         llm_run.end_time = datetime.utcnow()
@@ -246,7 +246,7 @@ def on_chain_start(
             execution_order=execution_order,
             child_execution_order=execution_order,
             child_runs=[],
-            run_type=RunTypeEnum.chain,
+            run_type="chain",
             tags=tags or [],
         )
         self._start_trace(chain_run)
@@ -259,7 +259,7 @@ def on_chain_end(
         if not run_id:
             raise TracerException("No run_id provided for on_chain_end callback.")
         chain_run = self.run_map.get(str(run_id))
-        if chain_run is None or chain_run.run_type != RunTypeEnum.chain:
+        if chain_run is None or chain_run.run_type != "chain":
             raise TracerException("No chain Run found to be traced")
 
         chain_run.outputs = outputs
@@ -279,7 +279,7 @@ def on_chain_error(
         if not run_id:
             raise TracerException("No run_id provided for on_chain_error callback.")
         chain_run = self.run_map.get(str(run_id))
-        if chain_run is None or chain_run.run_type != RunTypeEnum.chain:
+        if chain_run is None or chain_run.run_type != "chain":
             raise TracerException("No chain Run found to be traced")
 
         chain_run.error = repr(error)
@@ -316,7 +316,7 @@ def on_tool_start(
             execution_order=execution_order,
             child_execution_order=execution_order,
             child_runs=[],
-            run_type=RunTypeEnum.tool,
+            run_type="tool",
             tags=tags or [],
         )
         self._start_trace(tool_run)
@@ -327,7 +327,7 @@ def on_tool_end(self, output: str, *, run_id: UUID, **kwargs: Any) -> None:
         if not run_id:
             raise TracerException("No run_id provided for on_tool_end callback.")
         tool_run = self.run_map.get(str(run_id))
-        if tool_run is None or tool_run.run_type != RunTypeEnum.tool:
+        if tool_run is None or tool_run.run_type != "tool":
             raise TracerException("No tool Run found to be traced")
 
         tool_run.outputs = {"output": output}
@@ -347,7 +347,7 @@ def on_tool_error(
         if not run_id:
             raise TracerException("No run_id provided for on_tool_error callback.")
         tool_run = self.run_map.get(str(run_id))
-        if tool_run is None or tool_run.run_type != RunTypeEnum.tool:
+        if tool_run is None or tool_run.run_type != "tool":
             raise TracerException("No tool Run found to be traced")
 
         tool_run.error = repr(error)
@@ -386,7 +386,7 @@ def on_retriever_start(
             child_execution_order=execution_order,
             tags=tags,
             child_runs=[],
-            run_type=RunTypeEnum.retriever,
+            run_type="retriever",
         )
         self._start_trace(retrieval_run)
         self._on_retriever_start(retrieval_run)
@@ -402,7 +402,7 @@ def on_retriever_error(
         if not run_id:
             raise TracerException("No run_id provided for on_retriever_error callback.")
         retrieval_run = self.run_map.get(str(run_id))
-        if retrieval_run is None or retrieval_run.run_type != RunTypeEnum.retriever:
+        if retrieval_run is None or retrieval_run.run_type != "retriever":
             raise TracerException("No retriever Run found to be traced")
 
         retrieval_run.error = repr(error)
@@ -418,7 +418,7 @@ def on_retriever_end(
         if not run_id:
             raise TracerException("No run_id provided for on_retriever_end callback.")
         retrieval_run = self.run_map.get(str(run_id))
-        if retrieval_run is None or retrieval_run.run_type != RunTypeEnum.retriever:
+        if retrieval_run is None or retrieval_run.run_type != "retriever":
             raise TracerException("No retriever Run found to be traced")
         retrieval_run.outputs = {"documents": documents}
         retrieval_run.end_time = datetime.utcnow()

libs/langchain/langchain/callbacks/tracers/langchain.py

@@ -11,7 +11,7 @@
 from langsmith import Client
 
 from langchain.callbacks.tracers.base import BaseTracer
-from langchain.callbacks.tracers.schemas import Run, RunTypeEnum, TracerSession
+from langchain.callbacks.tracers.schemas import Run, TracerSession
 from langchain.env import get_runtime_environment
 from langchain.load.dump import dumpd
 from langchain.schema.messages import BaseMessage
@@ -107,7 +107,7 @@ def on_chat_model_start(
             start_time=start_time,
             execution_order=execution_order,
             child_execution_order=execution_order,
-            run_type=RunTypeEnum.llm,
+            run_type="llm",
             tags=tags,
         )
         self._start_trace(chat_model_run)

libs/langchain/langchain/callbacks/tracers/schemas.py

@@ -2,16 +2,27 @@
 from __future__ import annotations
 
 import datetime
+import warnings
 from typing import Any, Dict, List, Optional
 from uuid import UUID
 
 from langsmith.schemas import RunBase as BaseRunV2
-from langsmith.schemas import RunTypeEnum
+from langsmith.schemas import RunTypeEnum as RunTypeEnumDep
 from pydantic import BaseModel, Field, root_validator
 
 from langchain.schema import LLMResult
 
 
+def RunTypeEnum() -> RunTypeEnumDep:
+    """RunTypeEnum."""
+    warnings.warn(
+        "RunTypeEnum is deprecated. Please directly use a string instead"
+        " (e.g. 'llm', 'chain', 'tool').",
+        DeprecationWarning,
+    )
+    return RunTypeEnumDep
+
+
 class TracerSessionV1Base(BaseModel):
     """Base class for TracerSessionV1."""
 

libs/langchain/langchain/callbacks/tracers/wandb.py

@@ -15,7 +15,7 @@
 )
 
 from langchain.callbacks.tracers.base import BaseTracer
-from langchain.callbacks.tracers.schemas import Run, RunTypeEnum
+from langchain.callbacks.tracers.schemas import Run
 
 if TYPE_CHECKING:
     from wandb import Settings as WBSettings
@@ -154,11 +154,11 @@ def _convert_lc_run_to_wb_span(self, run: Run) -> "Span":
         :param run: The LangChain Run to convert.
         :return: The converted W&B Trace Span.
         """
-        if run.run_type == RunTypeEnum.llm:
+        if run.run_type == "llm":
             return self._convert_llm_run_to_wb_span(run)
-        elif run.run_type == RunTypeEnum.chain:
+        elif run.run_type == "chain":
             return self._convert_chain_run_to_wb_span(run)
-        elif run.run_type == RunTypeEnum.tool:
+        elif run.run_type == "tool":
             return self._convert_tool_run_to_wb_span(run)
         else:
             return self._convert_run_to_wb_span(run)

libs/langchain/langchain/smith/evaluation/runner_utils.py

@@ -22,7 +22,7 @@
 from urllib.parse import urlparse, urlunparse
 
 from langsmith import Client, RunEvaluator
-from langsmith.schemas import Dataset, DataType, Example, RunTypeEnum
+from langsmith.schemas import Dataset, DataType, Example
 
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.callbacks.manager import Callbacks
@@ -341,9 +341,9 @@ def _setup_evaluation(
         first_example, examples = _first_example(examples)
         if isinstance(llm_or_chain_factory, BaseLanguageModel):
             run_inputs, run_outputs = None, None
-            run_type = RunTypeEnum.llm
+            run_type = "llm"
         else:
-            run_type = RunTypeEnum.chain
+            run_type = "chain"
             if data_type in (DataType.chat, DataType.llm):
                 raise ValueError(
                     "Cannot evaluate a chain on dataset with "
@@ -370,13 +370,13 @@ def _setup_evaluation(
 def _determine_input_key(
     config: RunEvalConfig,
     run_inputs: Optional[List[str]],
-    run_type: RunTypeEnum,
+    run_type: str,
 ) -> Optional[str]:
     if config.input_key:
         input_key = config.input_key
         if run_inputs and input_key not in run_inputs:
             raise ValueError(f"Input key {input_key} not in run inputs {run_inputs}")
-    elif run_type == RunTypeEnum.llm:
+    elif run_type == "llm":
         input_key = None
     elif run_inputs and len(run_inputs) == 1:
         input_key = run_inputs[0]
@@ -391,15 +391,15 @@ def _determine_input_key(
 def _determine_prediction_key(
     config: RunEvalConfig,
     run_outputs: Optional[List[str]],
-    run_type: RunTypeEnum,
+    run_type: str,
 ) -> Optional[str]:
     if config.prediction_key:
         prediction_key = config.prediction_key
         if run_outputs and prediction_key not in run_outputs:
             raise ValueError(
                 f"Prediction key {prediction_key} not in run outputs {run_outputs}"
             )
-    elif run_type == RunTypeEnum.llm:
+    elif run_type == "llm":
         prediction_key = None
     elif run_outputs and len(run_outputs) == 1:
         prediction_key = run_outputs[0]
@@ -432,7 +432,7 @@ def _determine_reference_key(
 def _construct_run_evaluator(
     eval_config: Union[EvaluatorType, EvalConfig],
     eval_llm: BaseLanguageModel,
-    run_type: RunTypeEnum,
+    run_type: str,
     data_type: DataType,
     example_outputs: Optional[List[str]],
     reference_key: Optional[str],
@@ -472,7 +472,7 @@ def _construct_run_evaluator(
 
 def _load_run_evaluators(
     config: RunEvalConfig,
-    run_type: RunTypeEnum,
+    run_type: str,
     data_type: DataType,
     example_outputs: Optional[List[str]],
     run_inputs: Optional[List[str]],

libs/langchain/langchain/smith/evaluation/string_run_evaluator.py

@@ -5,7 +5,7 @@
 from typing import Any, Dict, List, Optional
 
 from langsmith import EvaluationResult, RunEvaluator
-from langsmith.schemas import DataType, Example, Run, RunTypeEnum
+from langsmith.schemas import DataType, Example, Run
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForChainRun,
@@ -327,7 +327,7 @@ async def aevaluate_run(
     def from_run_and_data_type(
         cls,
         evaluator: StringEvaluator,
-        run_type: RunTypeEnum,
+        run_type: str,
         data_type: DataType,
         input_key: Optional[str] = None,
         prediction_key: Optional[str] = None,
@@ -343,7 +343,7 @@ def from_run_and_data_type(
 
         Args:
             evaluator (StringEvaluator): The string evaluator to use.
-            run_type (RunTypeEnum): The type of run being evaluated.
+            run_type (str): The type of run being evaluated.
                 Supported types are LLM and Chain.
             data_type (DataType): The type of dataset used in the run.
             input_key (str, optional): The key used to map the input from the run.
@@ -361,9 +361,9 @@ def from_run_and_data_type(
         """  # noqa: E501
 
         # Configure how run inputs/predictions are passed to the evaluator
-        if run_type == RunTypeEnum.llm:
+        if run_type == "llm":
             run_mapper: StringRunMapper = LLMStringRunMapper()
-        elif run_type == RunTypeEnum.chain:
+        elif run_type == "chain":
             run_mapper = ChainStringRunMapper(
                 input_key=input_key, prediction_key=prediction_key
             )

libs/langchain/tests/unit_tests/callbacks/tracers/test_langchain_v1.py

@@ -17,7 +17,7 @@
     ToolRun,
     TracerSessionV1,
 )
-from langchain.callbacks.tracers.schemas import Run, RunTypeEnum, TracerSessionV1Base
+from langchain.callbacks.tracers.schemas import Run, TracerSessionV1Base
 from langchain.schema import LLMResult
 from langchain.schema.messages import HumanMessage
 
@@ -589,7 +589,7 @@ def test_convert_run(
         outputs=LLMResult(generations=[[]]).dict(),
         serialized={},
         extra={},
-        run_type=RunTypeEnum.llm,
+        run_type="llm",
     )
     chain_run = Run(
         id="57a08cc4-73d2-4236-8371-549099d07fad",
@@ -603,7 +603,7 @@ def test_convert_run(
         outputs={},
         child_runs=[llm_run],
         extra={},
-        run_type=RunTypeEnum.chain,
+        run_type="chain",
     )
 
     tool_run = Run(
@@ -618,7 +618,7 @@ def test_convert_run(
         serialized={},
         child_runs=[],
         extra={},
-        run_type=RunTypeEnum.tool,
+        run_type="tool",
     )
 
     expected_llm_run = LLMRun(