update testing

backend/retrieval_graph/configuration.py

@@ -22,7 +22,7 @@ class AgentConfiguration(BaseConfiguration):
     )
 
     response_model: str = field(
-        default="anthropic/claude-3-5-sonnet-20240620",
+        default="openai/gpt-4o-mini",
         metadata={
             "description": "The language model used for generating responses. Should be in the form: provider/model-name."
         },

backend/retrieval_graph/graph.py

@@ -6,7 +6,7 @@
 conducting research, and formulating responses.
 """
 
-from typing import Any, Literal, TypedDict, cast
+from typing import Any, Literal, Type, TypedDict, cast
 
 from langchain_core.messages import BaseMessage
 from langchain_core.runnables import RunnableConfig
@@ -33,6 +33,10 @@ async def analyze_and_route_query(
     Returns:
         dict[str, Router]: A dictionary containing the 'router' key with the classification result (classification type and logic).
     """
+    # allow skipping the router for testing
+    if state.router and state.router["logic"]:
+        return {"router": state.router}
+
     configuration = AgentConfiguration.from_runnable_config(config)
     model = load_chat_model(configuration.query_model)
     messages = [
@@ -207,22 +211,31 @@ async def respond(
 
 
 # Define the graph
-builder = StateGraph(AgentState, input=InputState, config_schema=AgentConfiguration)
-builder.add_node(analyze_and_route_query)
-builder.add_node(ask_for_more_info)
-builder.add_node(respond_to_general_query)
-builder.add_node(conduct_research)
-builder.add_node(create_research_plan)
-builder.add_node(respond)
-
-builder.add_edge(START, "analyze_and_route_query")
-builder.add_conditional_edges("analyze_and_route_query", route_query)
-builder.add_edge("create_research_plan", "conduct_research")
-builder.add_conditional_edges("conduct_research", check_finished)
-builder.add_edge("ask_for_more_info", END)
-builder.add_edge("respond_to_general_query", END)
-builder.add_edge("respond", END)
-
-# Compile into a graph object that you can invoke and deploy.
-graph = builder.compile()
-graph.name = "RetrievalGraph"
+
+
+def make_graph(*, input_schema: Type[Any]):
+    builder = StateGraph(
+        AgentState, input=input_schema, config_schema=AgentConfiguration
+    )
+    builder.add_node(analyze_and_route_query)
+    builder.add_node(ask_for_more_info)
+    builder.add_node(respond_to_general_query)
+    builder.add_node(conduct_research)
+    builder.add_node(create_research_plan)
+    builder.add_node(respond)
+
+    builder.add_edge(START, "analyze_and_route_query")
+    builder.add_conditional_edges("analyze_and_route_query", route_query)
+    builder.add_edge("create_research_plan", "conduct_research")
+    builder.add_conditional_edges("conduct_research", check_finished)
+    builder.add_edge("ask_for_more_info", END)
+    builder.add_edge("respond_to_general_query", END)
+    builder.add_edge("respond", END)
+
+    # Compile into a graph object that you can invoke and deploy.
+    graph = builder.compile()
+    graph.name = "RetrievalGraph"
+    return graph
+
+
+graph = make_graph(input_schema=InputState)

backend/tests/evals/test_e2e.py

@@ -1,3 +1,4 @@
+import asyncio
 from typing import Any
 
 import pandas as pd
@@ -6,10 +7,11 @@
 from langchain_core.messages import AIMessage
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.pydantic_v1 import BaseModel, Field
-from langsmith.evaluation import EvaluationResults, evaluate
+from langsmith.evaluation import EvaluationResults, aevaluate
 from langsmith.schemas import Example, Run
 
-from backend.retrieval_graph.graph import graph
+from backend.retrieval_graph.graph import make_graph
+from backend.retrieval_graph.state import AgentState, Router
 from backend.utils import format_docs
 
 DATASET_NAME = "chat-langchain-qa"
@@ -141,10 +143,16 @@ def evaluate_qa_context(run: Run, example: Example) -> dict:
 
 # Run evaluation
 
+# TODO: this is a hack to allow for skipping the router for testing. Add testing for individual components.
+graph = make_graph(input_schema=AgentState)
 
-def run_graph(inputs: dict[str, Any]) -> dict[str, Any]:
-    results = graph.invoke(
-        {"messages": [("human", inputs["question"])]},
+
+async def run_graph(inputs: dict[str, Any]) -> dict[str, Any]:
+    results = await graph.ainvoke(
+        {
+            "messages": [("human", inputs["question"])],
+            "router": Router(type="langchain", logic="The question is about LangChain"),
+        }
     )
     return results
 
@@ -162,13 +170,15 @@ def convert_single_example_results(evaluation_results: EvaluationResults):
 # NOTE: this is more of a regression test
 def test_scores_regression():
     # test most commonly used model
-    experiment_results = evaluate(
-        lambda inputs: run_graph(inputs),
-        data=DATASET_NAME,
-        evaluators=[evaluate_retrieval_recall, evaluate_qa, evaluate_qa_context],
-        experiment_prefix=EXPERIMENT_PREFIX,
-        metadata={"judge_model_name": JUDGE_MODEL_NAME},
-        max_concurrency=4,
+    experiment_results = asyncio.run(
+        aevaluate(
+            run_graph,
+            data=DATASET_NAME,
+            evaluators=[evaluate_retrieval_recall, evaluate_qa, evaluate_qa_context],
+            experiment_prefix=EXPERIMENT_PREFIX,
+            metadata={"judge_model_name": JUDGE_MODEL_NAME},
+            max_concurrency=4,
+        )
     )
     experiment_result_df = pd.DataFrame(
         convert_single_example_results(result["evaluation_results"])