Limit Retries in OpenAI

docs/source/notebooks/tool_usage/multiverse_math.ipynb

@@ -300,11 +300,11 @@
    ],
    "source": [
     "import uuid\n",
+    "\n",
     "from langsmith.client import Client\n",
     "\n",
     "from langchain_benchmarks.tool_usage import get_eval_config\n",
     "\n",
-    "\n",
     "experiment_uuid = uuid.uuid4().hex[:4]\n",
     "\n",
     "client = Client()\n",

langchain_benchmarks/extraction/evaluators.py

@@ -1,8 +1,18 @@
+from typing import Optional
+
+from langchain.chat_models import ChatOpenAI
 from langchain.chat_models.base import BaseChatModel
 from langchain.smith import RunEvalConfig
 
 
-def get_eval_config(eval_llm: BaseChatModel) -> RunEvalConfig:
+def get_eval_config(eval_llm: Optional[BaseChatModel] = None) -> RunEvalConfig:
+    eval_llm = eval_llm or ChatOpenAI(
+        model="gpt-4",
+        temperature=0,
+        model_kwargs={"seed": 42},
+        max_retries=1,
+        request_timeout=60,
+    )
     """Get the evaluation configuration for the email task."""
     return RunEvalConfig(
         evaluators=[

langchain_benchmarks/extraction/implementations.py

@@ -61,7 +61,13 @@ def run_on_dataset(
         kwargs: Additional arguments to pass to the client.
     """
     client = Client()
-    eval_llm = ChatOpenAI(model="gpt-4", temperature=0.0, model_kwargs={"seed": 42})
+    eval_llm = ChatOpenAI(
+        model="gpt-4",
+        temperature=0.0,
+        model_kwargs={"seed": 42},
+        max_retries=1,
+        request_timeout=60,
+    )
     return client.run_on_dataset(
         dataset_name=task.name,
         llm_or_chain_factory=create_openai_function_based_extractor(

langchain_benchmarks/rag/evaluators.py

@@ -84,10 +84,20 @@ def evaluate_run(
 
 def get_eval_config() -> RunEvalConfig:
     """Returns the evaluator for the environment."""
-    eval_llm = ChatOpenAI(model="gpt-4", temperature=0.0, model_kwargs={"seed": 42})
+    eval_llm = ChatOpenAI(
+        model="gpt-4",
+        temperature=0.0,
+        model_kwargs={"seed": 42},
+        max_retries=1,
+        request_timeout=60,
+    )
     # Use a longer-context LLM to check documents
     faithfulness_eval_llm = ChatOpenAI(
-        model="gpt-4-1106-preview", temperature=0.0, model_kwargs={"seed": 42}
+        model="gpt-4-1106-preview",
+        temperature=0.0,
+        model_kwargs={"seed": 42},
+        max_retries=1,
+        request_timeout=60,
     )
 
     return RunEvalConfig(

langchain_benchmarks/tool_usage/evaluators.py

@@ -99,7 +99,13 @@ class AgentTrajectoryEvaluator(RunEvaluator):
 
     def __init__(self) -> None:
         """Initialize the evaluator."""
-        eval_llm = ChatOpenAI(model="gpt-4", temperature=0, model_kwargs={"seed": 42})
+        eval_llm = ChatOpenAI(
+            model="gpt-4",
+            temperature=0,
+            model_kwargs={"seed": 42},
+            max_retries=1,
+            request_timeout=60,
+        )
         self.qa_evaluator = load_evaluator(EvaluatorType.QA, llm=eval_llm)
 
     def evaluate_run(